| 1. |
- Collares Pereira, Goncalo, et al.
(author)
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Linear Time-Varying Robust Model Predictive Control for Discrete-Time Nonlinear Systems
- 2018
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In: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 ; , s. 2659-2666
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Conference paper (peer-reviewed)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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| 2. |
- Kokogias, Stefanos, et al.
(author)
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Development of Platform-Independent System for Cooperative Automated Driving Evaluated in GCDC 2016
- 2018
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In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 19:4, s. 1277-1289
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Journal article (peer-reviewed)abstract
- Cooperative automated driving is a promising development in reducing energy consumption and emissions, increasing road safety, and improving traffic flow. The Grand Cooperative Driving Challenge (GCDC) 2016 was an implementation oriented project with the aim to accelerate research and development in the field. This paper describes the development of the two vehicle systems with which KTH participated in GCDC 2016. It presents a reference system architecture for collaborative automated driving as well as its instantiation on two conceptually different vehicles: a Scania truck and the research concept vehicle, built at KTH. We describe the common system architecture, as well as the implementation of a selection of shared and individual system functionalities, such as V2X communication, localization, state estimation, and longitudinal and lateral control. We also present a novel approach to trajectory tracking control for a four-wheel steering vehicle using model predictive control and a novel method for achieving fair data age distribution in vehicular communications.
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| 3. |
- Lima, Pedro F., 1990-, et al.
(author)
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Experimental validation of model predictive control stability for autonomous driving
- 2018
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In: Control Engineering Practice. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0967-0661 .- 1873-6939. ; 81, s. 244-255
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Journal article (peer-reviewed)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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| 4. |
- Lima, Pedro F., 1990-, et al.
(author)
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Progress Maximization Model Predictive Controller
- 2018
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In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 1075-1082
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Conference paper (peer-reviewed)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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| 5. |
- Oliveira, Rui Filipe De Sousa, et al.
(author)
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Path planning for autonomous bus driving in highly constrained environments
- 2019
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In: Proceedings 2019 IEEE Intelligent Transportation Systems Conference (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538670248 - 9781538670255 ; , s. 2743-2749
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Conference paper (peer-reviewed)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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| 6. |
- Pereira, Goncalo Collares
(author)
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Adaptive Lateral Model Predictive Control for Autonomous Driving of Heavy-Duty Vehicles
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Autonomous Vehicle (AV) technology promises safer, greener, and more efficient means of transportation for everyone. AVs are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where Heavy-Duty Vehicles (HDVs) operate. This thesis addresses lateral motion control for autonomous HDVs using Model Predictive Control (MPC). Lateral control for HDVs still has many open questions to be addressed, in particular, precise path tracking while ensuring a smooth, comfortable, and stable ride, coping with both external and internal disturbances, and adapting to different vehicles and conditions.To address these challenges, a comprehensive control module architecture is designed to adapt seamlessly to different vehicle types and interface with various planning and localization modules. Furthermore, it is designed to address system delays, maintain certain error bounds, and respect actuation constraints.This thesis presents the Reference Aware MPC (RA-MPC) for autonomous vehicles. This controller is iteratively improved throughout the thesis. The RA-MPC introduces a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller uses the linear time-varying MPC framework and considers control input rate and acceleration constraints to account for steering limitations. Furthermore, multiple models and control inputs are considered throughout the thesis. Ultimately, curvature acceleration is used as the control input, which together with stability ingredients, allows for stability guarantees under certain conditions via Lyapunov techniques.MPC is highly dependent on the prediction model used. This thesis proposes and compares different models. First, an offline-fitted, vehicle-specific nonlinear curvature response function is proposed and integrated into the kinematic bicycle model. The curvature response function is modeled as two Gaussian functions. To enhance the model's versatility and applicability to a fleet of vehicles the nonlinear curvature response table kinematic model is presented. This model replaces the function with a table, which is estimated online by means of Kalman filtering, adapting to the current vehicle and operating conditions.All controllers and models are simulated and experimentally validated on Scania HDVs and iteratively compared to the previous state-of-the-art. The RA-MPC with the nonlinear curvature response table kinematic model is shown to be the best for the problems and conditions considered. The robustness and adaptiveness of the proposed approach are highlighted by testing different vehicle configurations (a haulage truck, a mining truck, and a bus), operating conditions, and scenarios. The model allows all vehicles to accomplish the scenarios with very similar performance. Overall, the results show an average absolute lateral error to path no bigger than 7 cm, and a worst-case deviation no bigger than 25 cm. These results demonstrate the controller's ability to handle a fleet of HDVs, without the need for vehicle-specific tuning or intervention from expert engineers.
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| 7. |
- Pereira, Goncalo Collares, et al.
(author)
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Adaptive reference aware MPC for lateral control of autonomous vehicles
- 2023
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In: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 132
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Journal article (peer-reviewed)abstract
- This work addresses the design of a path tracking controller for autonomous vehicles. It reformulates the Reference Aware MPC in order to guarantee closed-loop stability, while maintaining a safe and comfortable ride, and minimizing wear and tear of vehicle components. Stability is proved via Lyapunov techniques. Furthermore, to adapt the response of the controller online while in operation, a novel model for the nonlinear curvature response of the vehicle is proposed. This model is estimated online by means of Kalman filtering. Both the proposed controller and curvature response model are evaluated with simulations and through experiments on a Scania construction truck, where the advantages to the previous state-of-the-art are highlighted and discussed.
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| 8. |
- Pereira, Goncalo Collares
(author)
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Lateral Model Predictive Control for Autonomous Heavy-Duty Vehicles : Sensor, Actuator, and Reference Uncertainties
- 2020
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Licentiate thesis (other academic/artistic)abstract
- Autonomous vehicle technology is shaping the future of road transportation. This technology promises safer, greener, and more efficient means of transportation for everyone. Autonomous vehicles are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where heavy-duty vehicles (HDVs) operate. Although research for autonomous systems has boomed in recent years, there are still many challenges associated with them. This thesis addresses lateral motion control for autonomous HDVs using model predictive control (MPC).First, the autonomous vehicle architecture and, in particular, the control module architecture are introduced. The control module receives the current vehicle states and a trajectory to follow, and requests a velocity and a steering-wheel angle to the vehicle actuators. Moreover, the control module needs to handle system delays, maintain certain error bounds, respect actuation constraints, and provide a safe and comfortable ride.Second, a linear robust model predictive controller for disturbed discrete-time nonlinear systems is presented. The optimization problem includes the initial nominal state of the system, which allows to guarantee robust exponential stability of the disturbance invariant set for the discrete-time nonlinear system. The controller effectiveness is demonstrated through simulations of an autonomous vehicle lateral control application. Finally, the controller limitations and possible improvements are discussed with the help of a more constrained autonomous vehicle example.Third, a path following reference aware MPC (RA-MPC) for autonomous vehicles is presented. The controller makes use of the linear time-varying MPC framework, and considers control input rates and accelerations to account for limitations on the vehicle steering dynamics and to provide a safe and comfortable ride. Moreover, the controller includes a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller is verified through simulations and through experiments with a Scania construction truck. The experiments show an average lateral error to path of around 7 cm, not exceeding 27 cm on dry roads.Finally, the nonlinear curvature response of the vehicle is studied and the MPC prediction model is modified to account for it. The standard kinematic bicycle model does not describe accurately the lateral motion of the vehicle. Therefore, by extending the model with a nonlinear function that maps the curvature response of the vehicle to a given request, a better prediction of the vehicle's movement is achieved. The modified model is used together with the RA-MPC and verified through simulations and experiments with a Scania construction truck, where the improvements of the more accurate model are verified. The experiments show an average lateral error to path of around 5 cm, not exceeding 20 cm on wet roads.
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| 9. |
- Pereira, Gonçalo Collares, et al.
(author)
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Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle
- 2017
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In: 2017 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509048045 ; , s. 310-316
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Conference paper (peer-reviewed)abstract
- In this paper, a lateral controller is proposed for an over-Actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-Actuated autonomous research vehicle.
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| 10. |
- Pereira, Goncalo Collares, et al.
(author)
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Nonlinear Curvature Modeling for MPC of Autonomous Vehicles
- 2020
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In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Conference paper (peer-reviewed)abstract
- This paper investigates how to compensate for curvature response mismatch in lateral Model Predictive Control (MPC) of an autonomous vehicle. The standard kinematic bicycle model does not describe accurately the vehicle yaw-rate dynamics, leading to inaccurate motion prediction when used in MPC. Therefore, the standard model is extended with a nonlinear function that maps the curvature response of the vehicle to a given request. Experimental data shows that a two Gaussian functions approximation gives an accurate description of this mapping. Both simulation and experimental results show that the corresponding modified model significantly improves the control performance when using Reference Aware MPC for autonomous driving of a Scania heavy-duty construction truck.
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