| 1. |
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| 2. |
- Carlemalm, Catharina, et al.
(författare)
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On the problem of blind equalization considering abrupt changes in the channel characteristics
- 2015
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Ingår i: European Signal Processing Conference. - : European Signal Processing Conference, EUSIPCO.
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Konferensbidrag (refereegranskat)abstract
- The problem of blind equalization in a digital communication system is considered. Unfortunately, the circuit might suffer from abrupt changes. Thus, it is critical not to ignore this phenomenon when the problem of blind equalization is analyzed. The proposed method, which is based on an Ito stochastic differential calculus approach, describes the dynamics of the output signal with an infinite impulse response (IIR) model where the involved taps are modeled as time-varying cadlag (con-tinu a droite limites a gauche) processes. Therefore, nonlinear and time-variant changes in the channel characteristics are included.
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| 3. |
- Collares Pereira, Goncalo, et al.
(författare)
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Linear Time-Varying Robust Model Predictive Control for Discrete-Time Nonlinear Systems
- 2018
-
Ingår i: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 ; , s. 2659-2666
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a robust model predictive controller for discrete-time nonlinear systems, subject to state and input constraints and unknown but bounded input disturbances. The prediction model uses a linearized time-varying version of the original discrete-time system. The proposed optimization problem includes the initial state of the current nominal model of the system as an optimization variable, which allows to guarantee robust exponential stability of a disturbance invariant set for the discrete-time nonlinear system. From simulations, it is possible to verify the proposed algorithm is real-time capable, since the problem is convex and posed as a quadratic program.
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| 4. |
- Ebadat, Afrooz, et al.
(författare)
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Application-oriented input design for room occupancy estimation algorithms
- 2017
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Ingår i: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - Piscataway, NJ : IEEE. - 9781509028733 - 9781509028740
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).
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| 5. |
- Ebadat, Afrooz, et al.
(författare)
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Model Predictive Control oriented experiment design for system identification : A graph theoretical approach
- 2017
-
Ingår i: Journal of Process Control. - : ELSEVIER SCI LTD. - 0959-1524 .- 1873-2771. ; 52, s. 75-84
-
Tidskriftsartikel (refereegranskat)abstract
- We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.
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| 6. |
- Hou, Jie, et al.
(författare)
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Subspace Hammerstein Model Identification under Periodic Disturbance
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963.
-
Konferensbidrag (refereegranskat)abstract
- In this paper, a subspace identification method is proposed for Hammerstein systems under periodic disturbance. By using the linear superposition principle to decompose the periodic disturbance response from the deterministic system response, an orthogonal projection is established to eliminate the disturbance effect. The unknown disturbance period can be estimated by defining an objective function of output prediction error for minimization. Correspondingly, a singular value decomposition (SVD) based algorithm is given to estimate the observability matrix and the lower triangular block-Toeplitz matrix. The state matrices A and C are subsequently retrieved from the estimated observability matrix via a shift-invariant algorithm, while the input matrix B and the nonlinear input function parameters are retrieved from the estimated lower triangular block-Toeplitz matrix by an SVD approach. Consistent estimation of the observability matrix and the lower triangular block-Toeplitz matrix is analyzed. An illustrative example is shown to demonstrate the effectiveness of the proposed identification method.
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| 7. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Experimental validation of model predictive control stability for autonomous driving
- 2018
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Ingår i: Control Engineering Practice. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0967-0661 .- 1873-6939. ; 81, s. 244-255
-
Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the design of time-varying model predictive control of an autonomous vehicle in the presence of input rate constraints such that closed-loop stability is guaranteed. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost to the controller formulation. The terminal set is the maximum positive invariant set of a multi-plant description of the vehicle linear time-varying model. The terminal cost is an upper-bound on the infinite cost-to-go incurred by applying a linear-quadratic regulator control law. The proposed control design is experimentally tested and successfully stabilizes an autonomous Scania construction truck in an obstacle avoidance scenario.
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| 8. |
- Lima, Pedro F., 1990-
(författare)
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Optimization-Based Motion Planning and Model Predictive Control for Autonomous Driving : With Experimental Evaluation on a Heavy-Duty Construction Truck
- 2018
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis addresses smooth motion planning and path following control of autonomous large and heavy industrial vehicles, such as trucks and buses, using optimization-based techniques. Autonomous driving is a rapidly expanding technology that promises to play an important role in future society, since it aims at more energy efficient, more convenient, and safer transport systems.First, we propose a clothoid-based path sparsification algorithm to describe a reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path.Second, we introduce a novel framework, in which path planning problems are posed in a convex optimization format, even when considering the vehicle dimension constraints, which maximizes the path planning performance in very constrained environments. Third, we present a progress maximization (i.e., traveling time minimization) model predictive controller for autonomous vehicles. The proposed controller optimizes the vehicle lateral and longitudinal motion simultaneously and its effectiveness is demonstrated, in simulation, even in the presence of obstacles.Fourth, we design a smooth and accurate model predictive controller tailored for industrial vehicles, where the main goal is to reduce the vehicle "wear and tear" during its operation. The controller effectiveness is shown both in simulation and experimentally in a Scania construction truck. We showed that the proposed controller has an extremely promising performance in real experiments.Fifth, we propose a novel terminal cost and a terminal state set in order to guarantee closed-loop stability when designing and implementing a linear time-varying model predictive controller for autonomous path following. The controller successfully stabilizes an autonomous Scania construction truck.
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| 9. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Progress Maximization Model Predictive Controller
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 1075-1082
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of progress maximization (i.e., traveling time minimization) along a given path for autonomous vehicles. Progress maximization plays an important role not only in racing, but also in efficient and safe autonomous driving applications. The progress maximization problem is formulated as a model predictive controller, where the vehicle model is successively linearized at each time step, yielding a convex optimization problem. To ensure real-time feasibility, a kinematic vehicle model is used together with several linear approximations of the vehicle dynamics constraints. We propose a novel polytopic approximation of the 'g-g' diagram, which models the vehicle handling limits by constraining the lateral and longitudinal acceleration. Moreover, the tire slip angles are restricted to ensure that the tires of the vehicle always operate in their linear force region by limiting the lateral acceleration. We illustrate the effectiveness of the proposed controller in simulation, where a nonlinear dynamic vehicle model is controlled to maximize the progress along a track, taking into consideration possible obstacles.
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| 10. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 2:4, s. 238-250
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an algorithm for lateral control of a vehicle – a smooth and accurate model predictive controller. The fundamental difference compared to a standard MPC is that the driving smoothness is directly addressed in the cost function. The controller objective is based on the minimization of the first- and second-order spatial derivatives of the curvature. By doing so, jerky commands to the steering wheel, which could lead to permanent damage on the steering components and vehicle structure, are avoided. A good path tracking accuracy is ensured by adding constraints to avoid deviations from the reference path. Finally, the controller is experimentally tested and evaluated on a Scania construction truck. The evaluation is performed at Scania’s facilities near So ̈derta ̈lje, Sweden via two different paths: a precision track that resembles a mining scenario and a high-speed test track that resembles a highway situation. Even using a linearized kinematic vehicle to predict the vehicle motion, the performance of the proposed controller is encouraging, since the deviation from the path never exceeds 30 cm. It clearly outperforms an industrial pure-pursuit controller in terms of path accuracy and a standard MPC in terms of driving smoothness.
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| 11. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving
- 2017
-
Ingår i: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. - : IEEE. - 9781509028733 ; , s. 2775-2782
-
Konferensbidrag (refereegranskat)abstract
- This paper presents stability conditions when designing a linear time-varying model predictive controller for lateral control of an autonomous vehicle. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost. We detail how to compute the terminal state and the terminal cost for the linear time-varying case, and interpret the obtained results in the light of an autonomous driving application. To determine the stability conditions, the concept of multi-model description is used, where the linear time-varying model is separated into a finite number of time- invariant models that depend on a single parameter. The terminal set is the maximum positive invariant set of the multi- model description and the terminal cost is the result of a min-max optimization that determines the worst time-invariant model if used as a prediction model. In fact, in the autonomous driving case, we show that the min-max approach is a convex optimization problem. The stability conditions are computed offline, maintain the convexity of the optimization, and do not affect the execution time of the controller. In simulation, we demonstrate the stabilizing effectiveness of the proposed conditions through an illustrative example of path following with a heavy-duty vehicle.
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| 12. |
- Mattila, Robert, et al.
(författare)
-
Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
- 2017
-
Ingår i: IEEE Signal Processing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1070-9908 .- 1558-2361. ; 24:12, s. 1813-1817
-
Tidskriftsartikel (refereegranskat)abstract
- We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. We propose a two-step algorithm: a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum-likelihood estimator. The two-fold contribution of this letter is, first, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and second, to numerically show that the method is computationally less demanding than conventional methods-in particular for large datasets.
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| 13. |
- Mattila, Robert, et al.
(författare)
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Estimating Private Beliefs of Bayesian Agents Based on Observed Decisions
- 2019
-
Ingår i: IEEE Control Systems Letters. - 2475-1456. ; , s. 523-528
-
Tidskriftsartikel (refereegranskat)abstract
- We consider sequential stochastic decision prob-lems in which, at each time instant, an agent optimizes itslocal utility by solving a stochastic program and, subsequently,announces its decision to the world. Given this action, we studythe problem of estimating the agent’s private belief (i.e., its posterior distribution over the set of states of nature basedon its private observations). We demonstrate that it is possible to determine the set of private beliefs that are consistent with public data by leveraging techniques from inverse optimization.We further give a number of useful characterizations of this set; for example, tight bounds by solving a set of linear programs (under concave utility). As an illustrative example, we consider estimating the private belief of an investor in regime-switching portfolio allocation. Finally, our theoretical resultsare illustrated and evaluated in numerical simulations.
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| 14. |
- Mattila, Robert, et al.
(författare)
-
Inverse filtering for hidden Markov models
- 2017
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Ingår i: Advances in Neural Information Processing Systems. - : Neural information processing systems foundation. ; , s. 4205-4214
-
Konferensbidrag (refereegranskat)abstract
- This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs). In particular, given a sequence of state posteriors and the system dynamics; i) estimate the corresponding sequence of observations, ii) estimate the observation likelihoods, and iii) jointly estimate the observation likelihoods and the observation sequence. We show how to avoid a computationally expensive mixed integer linear program (MILP) by exploiting the algebraic structure of the HMM filter using simple linear algebra operations, and provide conditions for when the quantities can be uniquely reconstructed. We also propose a solution to the more general case where the posteriors are noisily observed. Finally, the proposed inverse filtering algorithms are evaluated on real-world polysomnographic data used for automatic sleep segmentation.
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| 15. |
- Mattila, Robert, et al.
(författare)
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Inverse Filtering for Linear Gaussian State-Space Models
- 2018
-
Ingår i: Proceedings of the 57th IEEE Conference on Decision and Control (CDC’18), Miami Beach, FL, USA, 2018.. - 9781538613955
-
Konferensbidrag (refereegranskat)abstract
- This paper considers inverse filtering problemsfor linear Gaussian state-space systems. We consider threeproblems of increasing generality in which the aim is toreconstruct the measurements and/or certain unknown sensorparameters, such as the observation likelihood, given posteriors(i.e., the sample path of mean and covariance). The paperis motivated by applications where one wishes to calibratea Bayesian estimator based on remote observations of theposterior estimates, e.g., determine how accurate an adversary’ssensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e.g., measurementor quantization errors) in numerical simulations.
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| 16. |
- Muller, E. R., et al.
(författare)
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Optimal motion planning for automated vehicles with scheduled arrivals at intersections
- 2018
-
Ingår i: 2018 European Control Conference, ECC 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783952426982 ; , s. 1672-1678
-
Konferensbidrag (refereegranskat)abstract
- We design and compare three different optimal control strategies for the motion planning of automated vehicles approaching an intersection with scheduled arrivals. The objective is to minimize a combination of energy consumption and deviation from the schedule. The strategies differ in allowed deviations. When taking only vehicles inside the control region into account, the strategy that achieves the lowest energy consumption is the less strict one, albeit at the expense of higher travel times. When traffic conditions beyond the control region are considered, no strategy is able to achieve lower energy consumption or vehicle delay than the strategy that is the most strict in keeping with the schedule. Results suggests that in high traffic situations, from a global energy consumption standpoint, it is best to have vehicles crossing the intersection as soon as possible.
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| 17. |
- Oliveira, Rui Filipe De Sousa, et al.
(författare)
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Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications
- 2018
-
Ingår i: 2018 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2090-2097
-
Konferensbidrag (refereegranskat)abstract
- Lattice-based motion planners are an established method to generate feasible motions for car-like vehicles. However, the solution paths can only reach a discretized approximation of the intended goal pose. Moreover, they can be optimal only with respect to the actions available to the planner, which can result in paths with excessive steering. These drawbacks have a negative impact when used in real systems. In this paper we address both drawbacks by integrating a steering method into a state-of-the-art lattice-based motion planner. Un- like previous approaches, in which path optimization happens in an a posteriori step after the planner has found a solution, we propose an interleaved execution of path planning and path optimization. The proposed approach can run in real-time and is implemented in a full-size autonomous truck, and we show experimentally that it is able to greatly improve the quality of the solutions provided by a lattice planner.
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| 18. |
- Oliveira, Rui Filipe De Sousa, et al.
(författare)
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Path planning for autonomous bus driving in highly constrained environments
- 2019
-
Ingår i: Proceedings 2019 IEEE Intelligent Transportation Systems Conference (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538670248 - 9781538670255 ; , s. 2743-2749
-
Konferensbidrag (refereegranskat)abstract
- Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in highly constrained environments, such as urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and accurate collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method.
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| 19. |
- Oliveira, Rui Filipe De Sousa, et al.
(författare)
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Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy-Duty Vehicles
- 2018
-
Ingår i: 2018 European Control Conference, ECC 2018. - : Institute of Electrical and Electronics Engineers Inc.. - 9783952426982 ; , s. 935-940
-
Konferensbidrag (refereegranskat)abstract
- We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth driving of autonomous heavy-duty vehicles, which are characterized by slow actuator dynamics. In order to deal with the slow dynamics, we take into account rate and, additionally, torque limitations of the steering actuator directly. Previous methods only take into account limitations in the path curvature, which deals indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic curvature paths together with circular arcs to steer the vehicle. The obtained paths are characterized by a smooth and continuously differentiable steering angle profile. The final trajectories computed with our method provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by fast computation times. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real-time capabilities.
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| 20. |
- Oliveira, Rui, 1991-
(författare)
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Motion Planning for Heavy-Duty Vehicles
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous driving is a disrupting technology that is expected to reshape transportation systems. The benefits of autonomous vehicles include, but are not limited to, safer transportation, increased economic growth, and broader access to mobility services. Industry and academia are currently researching a variety of topics related to autonomous driving, however, the focus seems to be on passenger vehicles. As a consequence, heavy-duty vehicles, which are a significant share of transportation systems, are overlooked, and the challenges associated with these vehicles are neglected.This thesis studies motion planning algorithms for heavy-duty vehicles. Motion planning is a fundamental part of autonomous vehicles, it is tasked with finding the correct sequence of actions that take the vehicle towards its goal. This work focuses on particular aspects that distinguish heavy-duty vehicles from passenger vehicles, and that call for novel developments within motion planning algorithms.We start by addressing the problem of finding shortest paths for a vehicle in obstacle-free environments. This problem has been studied since the fifties, but the addressed vehicle models are often simplistic. We propose a novel algorithm that is able to plan paths respecting complex vehicle actuator constraints associated with the slow dynamics of heavy vehicles.Using the previous method, we tackle the motion planning problem in environments populated with obstacles. Lattice-based motion planners, a popular choice for this type of scenario, come with drawbacks related to the sub-optimality of solution paths, and the discretization of the goal state. We propose a novel path optimization method, which is able to significantly reduce both problems. The resulting optimized paths contain less oscillatory behavior and arrive precisely at arbitrary non-discretized goal states.We then study the problem of bus driving in urban environments. It is shown how this type of driving is fundamentally different than that of other vehicles, due to the chassis configuration with large overhangs. To successfully maneuver buses, distinct driving objectives need to be used in planning algorithms. Moreover, a novel environment classification scheme must be introduced. The result is a motion planning algorithm that is able to mimic professional bus driver behavior, resulting in safer driving and increased vehicle maneuverability.
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| 21. |
- Persson, Linnea, 1992-
(författare)
-
Autonomous and Cooperative Landings Using Model Predictive Control
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cooperation is increasingly being applied in the control of interconnected multi-agent systems, and it introduces many benefits. In particular, cooperation can improve the efficiency of many types of missions, and adds flexibility and robustness against external disturbances or unknown obstacles. This thesis investigates cooperative maneuvers for aerial vehicles autonomously landing on moving platforms, and how to safely and robustly perform such landings on a real system subject to a variety of disturbances and physical and computational constraints. Two specific examples are considered: the landing of a fixed-wing drone on top of a moving ground carriage; and the landing of a quadcopter on a boat. The maneuvers are executed in a cooperative manner where both vehicles are allowed to take actions to reach their common objective while avoiding safety based spatial constraints. Applications of such systems can be found in, for example, autonomous deliveries, emergency landings, and search and rescue missions. Particular challenges of cooperative landing maneuvers include the heterogeneous and nonlinear dynamics, the coupled control, the sensitivity to disturbances, and the safety criticality of performing a high-velocity landing maneuver.The thesis suggests the design of a cooperative control algorithm for performing autonomous and cooperative landings. The algorithm is based on model predictive control, an optimization-based method where at every sampling instant a finite-horizon optimal control problem is solved. The advantages of applying this control method in this setting arise from its ability to include explicit dynamic equations, constraints, and disturbances directly in the computation of the control inputs. It is shown how the resulting optimization problem of the autonomous landing controller can be decoupled into a horizontal and a vertical sub-problem, a finding which significantly increases the efficiency of the algorithm. The algorithm is derived for two different autonomous landing systems, which are subsequently implemented in realistic simulations and on a drone for real-world flight tests. The results demonstrate both that the controller is practically implementable on real systems with computational limitations, and that the suggested controller can successfully be used to perform the cooperative landing under the influence of external disturbances and under the constraint of various safety requirements.
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| 22. |
- Persson, Linnea, et al.
(författare)
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Cooperative Rendezvous of Ground Vehicle and Aerial Vehicle using Model Predictive Control
- 2017
-
Ingår i: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781509028733 ; , s. 2819-2824
-
Konferensbidrag (refereegranskat)abstract
- This paper considers the problem of controlling a fixed-wing unmanned aerial vehicle and a cooperating unmanned ground vehicle to rendezvous by making the aerial vehicle land on top the ground vehicle. Both vehicles are actively taking part in the control effort, where they coordinate positions and velocities to complete the landing. The rendezvous time and the terminal state are kept free to increase the flexibility of the solution. There are two main challenges with this maneuver. First, the controller must force the system to stay within a safe set such that the aerial vehicle approaches the ground vehicle directly from above. Second, the rendezvous must occur within some finite distance. A model predictive control algorithm is proposed to achieve these objectives. The choice is motivated by recent experimental results showing how the landing safety and efficiency could benefit from including safety margins already in the computation of the control inputs. A controller, which steers the agents towards rendezvous and which indirectly provides safety guarantees through non-convex optimization constraints, is derived. Simulations are provided showing the ability of the controller to plan a safe trajectory online, even under the disturbance of wind gusts.
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| 23. |
- Persson, Linnea, 1992-, et al.
(författare)
-
Model predictive control for autonomous ship landing in a search and rescue scenario
- 2019
-
Ingår i: Model predictive control for autonomous ship landing in a search and rescue scenario. - San Diego : American Institute of Aeronautics and Astronautics. ; , s. 1169-
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a Model Predictive Control approach for autonomous landing of a quadcopter on the deck of a moving boat. The research is motivated by a large-scale demonstrator arena equipped with autonomous boats and drones that should collaborate to perform various tasks related to search and rescue missions. The landing maneuver is executed in a cooperative manner where both the boat and the drone take actions to reach their common objective. The maneuver is designed to be feasible under a range of conditions, including scenarios where the boat is moving across the water or when it is subjected to disturbances such as waves and winds. During the landing, the vehicles must also consider various safety constraints for landing safely and efficiently. The algorithms are implemented both in hardware-in-the-loop simulations, where we demonstrate some of the different scenarios that the algorithm is expected to handle, as well as on a real boat-drone system, on which initial tests have been carried out.
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| 24. |
- Persson, Linnea, 1992-, et al.
(författare)
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Verification of Cooperative Maneuvers in FlightGear using MPC and Backwards Reachable Sets
- 2018
-
Ingår i: 2018 European Control Conference, ECC 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783952426982 ; , s. 1411-1416
-
Konferensbidrag (refereegranskat)abstract
- In this paper we develop a simulation setup for testing and analyzing cooperative maneuvers and corresponding control algorithms. We also find feasible initial sets using backwards reachable set computations for the cooperative control problem, which we then test using the simulation setup. The particular example considered is a cooperative rendezvous between a fixed-wing unmanned aerial vehicle and a unmanned ground vehicle. The open-source software FlightGear and JSBSim are used for the vehicle dynamics, enabling testing of algorithms in a realistic environment. The aircraft models include nonlinear, state-dependent dynamics, making it possible to capture complex behaviors like stall and spin. Moreover, environmental effects such as wind gusts and turbulence are directly integrated into the simulations. From the simulations we get a comprehensive understanding of the controller performance and feasibility when tested in a real-time scenario. Results from several landing simulations are presented, and demonstrate that the MPC solution for the cooperative rendezvous problem is a promising method also for use in complex, safety-critical systems.
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| 25. |
- Plessen, Mogens Graf, et al.
(författare)
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Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming
- 2017
-
Ingår i: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781538615263
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space.
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| 26. |
- Sadeghi, Mostafa, et al.
(författare)
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A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization
- 2018
-
Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 51:15, s. 96-101
-
Tidskriftsartikel (refereegranskat)abstract
- We consider identification of linear systems with a certain order from a set of noisy input-output observations. We utilize the fact that the system order corresponds to the rank of the Hankel matrix associated with the system impulse response. Then, the system identification problem is formulated as the minimization of the output error subject to a rank constraint on a Hankel matrix. As this problem is non-convex, we propose a branch and bound (BB) solver, which is a powerful tool for solving non-convex problems to optimality. The main ingredients of the proposed BB method are a convex relaxation problem and a local minimizer of the original non-convex problem. We illustrate the promising performance of the proposed scheme in a system identification problem. The results demonstrate the higher accuracy and stability of our method in estimating the true system compared to the standard output error (OE) algorithm.
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| 27. |
- Wahlberg, Bo, 1959-, et al.
(författare)
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Algorithms and Performance Analysis for Stochastic Wiener System Identification
- 2018
-
Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 2475-1456. ; 2:3, s. 471-476
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Tidskriftsartikel (refereegranskat)abstract
- We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear dynamics, process noise and measured by a non-linear sensor with additive measurement noise. There are many possible system identification methods for such systems, including the maximum likelihood (ML) method and the prediction error method. The focus has mostly been on algorithms and implementation, and less is known about the statistical performance and the corresponding Cramér-Rao lower bound (CRLB) for identification of such non-linear systems. We derive expressions for the CRLB and the asymptotic normalized covariance matrix for certain Gaussian approximations of Wiener systems to show how a non-linear sensor affects the accuracy compared to a corresponding linear sensor. The key idea is to take second order statistics into account by using a common parametrization of the mean and the variance of the output process. This analysis also leads to an ML motivated identification method based on the conditional mean predictor and a Gaussian distribution approximation. The analysis is supported by numerical simulations.
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