4,787 research outputs found
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees
Infinite-horizon optimal control of constrained piecewise affine (PWA)
systems has been approximately addressed by hybrid model predictive control
(MPC), which, however, has computational limitations, both in offline design
and online implementation. In this paper, we consider an alternative approach
based on approximate dynamic programming (ADP), an important class of methods
in reinforcement learning. We accommodate non-convex union-of-polyhedra state
constraints and linear input constraints into ADP by designing PWA penalty
functions. PWA function approximation is used, which allows for a mixed-integer
encoding to implement ADP. The main advantage of the proposed ADP method is its
online computational efficiency. Particularly, we propose two control policies,
which lead to solving a smaller-scale mixed-integer linear program than
conventional hybrid MPC, or a single convex quadratic program, depending on
whether the policy is implicitly determined online or explicitly computed
offline. We characterize the stability and safety properties of the closed-loop
systems, as well as the sub-optimality of the proposed policies, by quantifying
the approximation errors of value functions and policies. We also develop an
offline mixed-integer linear programming-based method to certify the
reliability of the proposed method. Simulation results on an inverted pendulum
with elastic walls and on an adaptive cruise control problem validate the
control performance in terms of constraint satisfaction and CPU time
Model predictive control techniques for hybrid systems
This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
The majority of current studies on autonomous vehicle control via deep
reinforcement learning (DRL) utilize point-mass kinematic models, neglecting
vehicle dynamics which includes acceleration delay and acceleration command
dynamics. The acceleration delay, which results from sensing and actuation
delays, results in delayed execution of the control inputs. The acceleration
command dynamics dictates that the actual vehicle acceleration does not rise up
to the desired command acceleration instantaneously due to dynamics. In this
work, we investigate the feasibility of applying DRL controllers trained using
vehicle kinematic models to more realistic driving control with vehicle
dynamics. We consider a particular longitudinal car-following control, i.e.,
Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass
kinematic model. When such a controller is applied to car following with
vehicle dynamics, we observe significantly degraded car-following performance.
Therefore, we redesign the DRL framework to accommodate the acceleration delay
and acceleration command dynamics by adding the delayed control inputs and the
actual vehicle acceleration to the reinforcement learning environment state,
respectively. The training results show that the redesigned DRL controller
results in near-optimal control performance of car following with vehicle
dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc
Reinforcement Learning, Intelligent Control and their Applications in Connected and Autonomous Vehicles
Reinforcement learning (RL) has attracted large attention over the past few years. Recently, we developed a data-driven algorithm to solve predictive cruise control (PCC) and games output regulation problems. This work integrates our recent contributions to the application of RL in game theory, output regulation problems, robust control, small-gain theory and PCC. The algorithm was developed for adaptive optimal output regulation of uncertain linear systems, and uncertain partially linear systems to reject disturbance and also force the output of the systems to asymptotically track a reference. In the PCC problem, we determined the reference velocity for each autonomous vehicle in the platoon using the traffic information broadcasted from the lights to reduce the vehicles\u27 trip time. Then we employed the algorithm to design an approximate optimal controller for the vehicles. This controller is able to regulate the headway, velocity and acceleration of each vehicle to the desired values. Simulation results validate the effectiveness of the algorithms
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