| 1. |
- Pereira, Goncalo Collares
(författare)
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Adaptive Lateral Model Predictive Control for Autonomous Driving of Heavy-Duty Vehicles
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 2. |
- Abdalmoaty, Mohamed, 1986-, et al.
(författare)
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The Gaussian MLE versus the Optimally weighted LSE
- 2020
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Ingår i: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 37:6, s. 195-199
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Tidskriftsartikel (refereegranskat)abstract
- In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.
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| 3. |
- Blomberg, Niclas, 1986-, et al.
(författare)
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Approximate Regularization Paths for Nuclear Norm Minimization using Singular Value Bounds : with Implementation and Extended Appendix
- 2015
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Konferensbidrag (refereegranskat)abstract
- The widely used nuclear norm heuristic for rank minimizationproblems introduces a regularization parameter which isdifficult to tune. We have recently proposed a method to approximatethe regularization path, i.e., the optimal solution asa function of the parameter, which requires solving the problemonly for a sparse set of points. In this paper, we extendthe algorithm to provide error bounds for the singular valuesof the approximation. We exemplify the algorithms on largescale benchmark examples in model order reduction. Here,the order of a dynamical system is reduced by means of constrainedminimization of the nuclear norm of a Hankel matrix.
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| 4. |
- Blomberg, Niclas, 1986-
(författare)
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On Nuclear Norm Minimization in System Identification
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In system identification we model dynamical systems from measured data. This data-driven approach to modelling is useful since many real-world systems are difficult to model with physical principles. Hence, a need for system identification arises in many applications involving simulation, prediction, and model-based control.Some of the classical approaches to system identification can lead to numerically intractable or ill-posed optimization problems. As an alternative, it has recently been shown beneficial to use so called regularization techniques, which make the ill-posed problems ‘regular’. One type of regularization is to introduce a certain rank constraint. However, this in general still leads to a numerically intractable problem, since the rank function is non-convex. One possibility is then use a convex approximation of rank, which we will do here.The nuclear norm, i.e., the sum of the singular values, is a popular, convex surrogate of the rank function. This results in a heuristic that has been widely used in e.g. signal processing, machine learning, control, and system identification, since its introduction in 2001. The nuclear norm heuristic introduces a regularization parameter which governs the trade-off between model fit and model complexity. The parameter is difficult to tune, and thecurrent thesis revolves around this issue.In this thesis, we first propose a choice of the regularization parameter based on the statistical properties of fictitious validation data. This can be used to avoid computationally costly techniques such as cross-validation, where the problem is solved multiple times to find a suitable parameter value. The proposed choice can also be used as initialization to search methods for minimizing some criterion, e.g. a validation cost, over the parameter domain.Secondly, we study how the estimated system changes as a function of the parameter over its entire domain, which can be interpreted as a sensitivity analysis. For this we suggest an algorithm to compute a so called approximate regularization path with error guarantees, where the regularization path is the optimal solution as a function of the parameter. We are then able to guarantee the model fit, or, alternatively, the nuclear norm of the approximation, to deviate from the optimum by less than a pre-specified tolerance. Furthermore, we bound the l2-norm of the Hankel singular value approximation error, which means that in a certain subset of the parameter domain, we can guarantee the optimal Hankel singular values returned by the nuclear norm heuristic to not change more (in l2-norm) than a bounded, known quantity.Our contributions are demonstrated and evaluated by numerical examples using simulated and benchmark data.
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| 5. |
- Blomqvist, Anders, et al.
(författare)
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On frequency weighting in autoregressive spectral estimation
- 2005
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Ingår i: IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 0780388747 ; , s. 245-248
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Konferensbidrag (refereegranskat)abstract
- This paper treats the problem of approximating a complex stochastic process in a given frequency region by an estimated autoregressive (AR) model. Two frequency domain approaches are discussed: a weighted frequency domain maximum likelihood method and a prefiltered covariance extension method based on the theory of Lindquist and co-workers. It is shown that these two approaches are very closely related and can both be formulated as convex optimization problems. An examples illustrating the methods and the effect of prefiltering/weighting is provided. The results show that these methods are capable of tuning the AR model fit to a specified frequency region.
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| 6. |
- Egidio, Lucas N., et al.
(författare)
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Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
- 2021
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Ingår i: 2021 international joint conference on neural networks (IJCNN). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780738133669
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Konferensbidrag (refereegranskat)abstract
- We consider the problem to learn a step-size policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. This is a limited computational memory quasi-Newton method widely used for deterministic unconstrained optimization. However, L-BFGS is currently avoided in large-scale problems for requiring step sizes to be provided at each iteration. Current methodologies for the step size selection for L-BFGS use heuristic tuning of design parameters and massive re-evaluations of the objective function and gradient to find appropriate step-lengths. We propose a neural network architecture with local information of the current iterate as the input. The step-length policy is learned from data of similar optimization problems, avoids additional evaluations of the objective function, and guarantees that the output step remains inside a pre-defined interval. The corresponding training procedure is formulated as a stochastic optimization problem using the backpropagation through time algorithm. The performance of the proposed method is evaluated on the training of image classifiers for the MNIST database for handwritten digits and for CIFAR-10. The results show that the proposed algorithm outperforms heuristically tuned optimizers such as ADAM, RMSprop, L-BFGS with a backtracking line search, and L-BFGS with a constant step size. The numerical results also show that a learned policy can be used as a warm-start to train new policies for different problems after a few additional training steps, highlighting its potential use in multiple large-scale optimization problems.
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| 7. |
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| 8. |
- Jansson, Magnus, et al.
(författare)
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On Consistency of Subspace System Identification Methods
- 1996
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Ingår i: IFAC World Congress. ; , s. 181-186
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Konferensbidrag (refereegranskat)abstract
- System identification of linear dynamical systems using so-called subspace methods consists of two main steps. First, a signal subspace estimate is found. This usually corresponds to estimating the range space of the extended observability matrix. Then the system parameters are estimated from the subspace estimate. The main result of this note is explicit excitation conditions on the input signals, which guarantee consistent estimates of the range space of the extended observability matrix.
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| 9. |
- Mattila, Robert, et al.
(författare)
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Inverse Filtering for Linear Gaussian State-Space Models
- 2018
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Ingår i: Proceedings of the 57th IEEE Conference on Decision and Control (CDC’18), Miami Beach, FL, USA, 2018.. - 9781538613955
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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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| 10. |
- Mattila, Robert, et al.
(författare)
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What did your adversary believe? : Optimal filtering and smoothing in counter-adversarial autonomous systems
- 2020
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Ingår i: 2020 IEEE international conference on acoustics, speech, and signal processing. - : IEEE. ; , s. 5495-5499
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Konferensbidrag (refereegranskat)abstract
- We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our current state via a noisy sensor, ii) computes a posterior estimate (belief) and iii) takes an action that we can observe. Based on such observed actions and our knowledge of our state sequence, we aim to estimate the adversary's past and current beliefs - this forms a foundation for predicting, and counteracting against, future actions. We derive the optimal smoother for the adversary's beliefs (we treat the problem in a Bayesian framework). Moreover, we demonstrate how the smoother can be computed for discrete systems even though the corresponding backward variables do not admit a finite-dimensional characterization. Finally, we illustrate our results in numerical simulations.
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