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We present a general approach to model the motion of a rigid body ground vehicle on a smooth nonplanar road surface. We generalise planar control-oriented models while preserving standard model states, inputs and outputs. We derive our approach from first principles, apply it to existing vehicle modelling approaches and test it through real-world experiments. We demonstrate that our approach improves upon the prediction accuracy of planar models. [ABSTRACT FROM AUTHOR]

This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectories solving control tasks in different environments. In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories. Our goal is to use these trajectories to find a safe and high-performing policy for a new task in a new, unknown environment. We propose using the stored data to learn generalizable control strategies. At each time step, based on a local forecast of the new task environment, the learned strategy consists of a target region in the state space and input constraints to guide the system evolution to the target region. These target regions are used as terminal sets by a low-level model predictive controller. We show how to i) design the target sets from past data and then ii) incorporate them into a model predictive control scheme with shifting horizon that ensures safety of the closed-loop system when performing the new task. We prove the feasibility of the resulting control policy, and apply the proposed method to robotic path planning, racing, and computer game applications. Note to Practitioners—This paper was motivated by the challenge of designing safe controllers for autonomous systems navigating through new environments. We consider scenarios where trajectory data from control tasks in different environments is available to the control designer. Possible applications include autonomous vehicles racing on new tracks or robotic manipulators performing tasks in the presence of new obstacles. Existing approaches to model-based control design for new environments generally use trajectory libraries, systematically adapting stored trajectories to the constraints of the new environment. This typically requires a priori knowledge of the entire task environment as well as resources to store and maintain the growing library. This paper suggests a new hierarchical control approach, in which stored trajectories are used to learn high-level strategies that can be applied while solving the new task. The strategies are learned offline, and only the parameterized strategy function needs to be stored for online control. Strategies only require knowledge of the nearby task environment, and provide navigation guidelines for the system. In this paper we show how to find such strategies from previous task data and how to integrate them into a low-level controller to safely and efficiently solve the new task. We also show how to adapt the modular framework as needed for a user’s desired application. Simulation experiments in robotic manipulator, autonomous vehicle, and computer game examples suggest that our approach can be used in a wide range of applications. In future research, we will address how to adapt the method for time-varying or stochastic environments. [ABSTRACT FROM AUTHOR]

Summary: In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed‐loop system. We show how to construct a time varying safe set and terminal cost function using closed‐loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non‐decreasing performance. Computational efficiency is obtained by convexifing the time‐varying safe set and time‐varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel framework for approximating the MPC policy for linear parameter-varying systems using supervised learning. Our learning scheme guarantees feasibility and near-optimality of the approximated MPC policy with high probability. Furthermore, in contrast to most existing approaches that only learn the MPC policy, we also learn the “dual policy,” which enables us to keep a check on the approximated MPC’s optimality online during the control process. If the check deems the control input from the approximated MPC policy safe and near-optimal, then it is applied to the plant; otherwise, a backup controller is invoked, thus filtering out (severely) suboptimal control inputs. The backup controller is only invoked with a bounded (low) probability, where the exact probability level can be chosen by the user. Since our framework does not require solving any optimization problem during the control process, it enables the deployment of MPC on resource-constrained systems. Specifically, we illustrate the utility of the proposed framework on a vehicle dynamics control problem. Compared with online optimization methods, we demonstrate a speedup of up to 62 × on a desktop computer and 10 × on an automotive-grade electronic control unit, while maintaining high control performance. [ABSTRACT FROM AUTHOR]

We present a learning model predictive controller (LMPC) for autonomous racing. We model the autonomous racing problem as a minimum time iterative control task, where an iteration corresponds to a lap. The system trajectory and input sequence of each lap are stored and used to systematically update the controller for the next lap. In the proposed approach, the race time does not increase at each iteration. The first contribution is to propose a local LMPC which reduces the computational burden associated with existing LMPC strategies. In particular, we show how to construct a local safe set and approximation to the value function, using a subset of the stored data. The second contribution is to present a system identification strategy for the autonomous racing iterative control task. We use data from previous iterations and the vehicle’s kinematic equations of motion to build an affine time-varying prediction model. The effectiveness of the proposed strategy is demonstrated by experimental results on the Berkeley Autonomous Race Car (BARC) platform. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach in train control systems based on the concept called virtual coupling or train convoys. This approach follows the recent developments in the field of safe platooning of autonomous vehicles. We use a decentralized model predictive control (MPC) framework for each train participating in a convoy formation. Control designs for the leading and following trains are presented. An optimal control formulation for both controllers is used and its design relies on a numeric solution of a finite horizon optimal control problem. This paper compares the proposed method with alternative control strategies including the well-studied moving block train control concept. A study of a metro line has been chosen as a first analysis for this control approach. The simulation results for this kind of railway lines demonstrate better performance and the benefits of this new concept versus the moving block system. We show that the virtual coupling concept substantially reduces headway and distance between trains while guaranteeing safe separation between two consecutive trains at any instant. [ABSTRACT FROM AUTHOR]

This paper presents a novel design of control algorithms for lane change assistance and autonomous driving on highways, based on recent results in Scenario Model Predictive Control (SCMPC). The basic idea is to account for the uncertainty in the traffic environment by a small number of future scenarios, which is intuitive and computationally efficient. These scenarios can be generated by any model-based or data-based approach. The paper discusses the SCMPC design procedure, which is simple and can be generalized to other control challenges in automated driving, as well as the controller's robustness properties. Experimental results demonstrate the effectiveness of the SCMPC algorithm and its performance in lane change situations on highways. [ABSTRACT FROM PUBLISHER]

Marine vessels with dynamic positioning (DP) capability are typically equipped with sufficient number of thrusters to make them overactuated and with satellite navigation and other sensors to determine their position, heading, and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a DP control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider a model-predictive control (MPC) algorithm that combines positioning control and TA into a single algorithm, which should theoretically yield a near-optimal controller output. This paper explores the advantages and disadvantages of using MPC compared with the traditional algorithms. [ABSTRACT FROM PUBLISHER]

Threat assessment during semiautonomous driving is used to determine when correcting a driver's input is required. Since current semiautonomous systems perform threat assessment by predicting a vehicle's future state while treating the driver's input as a disturbance, autonomous controller intervention is limited to a restricted regime. Improving vehicle safety demands threat assessment that occurs over longer prediction horizons wherein a driver cannot be treated as a malicious agent. In this paper, we describe a real-time semiautonomous system that utilizes empirical observations of a driver's pose to inform an autonomous controller that corrects a driver's input when possible in a safe manner. We measure the performance of our system using several metrics that evaluate the informativeness of the prediction and the utility of the intervention procedure. A multisubject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semiautonomous system. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper a receding horizon control approach for multi-product production plants is presented. Specifically two-stage plants are considered. In the first stage, a set of parallel production lines generates intermediate products from raw materials. In the second stage, the intermediate products are assembled into final products. A set of buffers for the intermediate products connects the production lines and the assembly line thus allowing a continuous production flow. The focus is on plants where the switch between product types is less frequent than in the assembly line. The latter is mostly dictated by the external demand, while the first one is the main scheduling variable. A systematic event-based control approach using receding horizon control (RHC) techniques is proposed; specifically the production line flow is controlled in order to satisfy the time-varying request from the assembly line while minimizing the intermediate products storage and processing time. Experimental results underline the benefits resulting from the application of the proposed approach to a car engine manufacturing process. [Copyright &y& Elsevier]