Sub‐optimal switching in anti‐lock brake systems using approximate dynamic programming

Optimal tracking in switched systems with controlled subsystem and Discrete-time (DT) dynamics is investigated. A feedback control policy is generated such that a) the system tracks the desired reference signal, and b) the optimal switching time instants are sought. For finding the optimal solution, approximate dynamic programming is used. Simulation results are provided to illustrate the effectiveness of the solution.

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“…The inclusion of continuous control, i.e., u(. ), in (1) shows that the subsystems are controlled subsystems.…”

Section: Problem Formulationmentioning

confidence: 98%

“…Due to the discontinuous nature of the switched systems, control of these systems are challenging. Control of switched systems addresses many critical problems in automotive engineering [1], power engineering [2], [3], and many other engineering fields.…”

Section: Introductionmentioning

confidence: 99%

Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Sardarmehni

Song

2019

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive

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“…Value Iteration (VI) is one popular approximate dynamic programming [1][2][3][4][5][6][7] and reinforcement learning algorithm [8][9][10][11][12][13], together with Policy Iteration. VI Reinforcement Learning (VIRL) algorithm comes in many implementation flavors, online or offline, offpolicy or on-policy, batch-wise or adaptive-wise, with known or unknown system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

Rădac

Borlea

2021

Energies

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In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable system, a new state representation in terms of input/output (IO) data is derived. Consequently, the Virtual State Feedback Tuning (VRFT)-based solution is redefined to accommodate virtual state feedback control, leading to an original stability-certified Virtual State-Feedback Reference Tuning (VSFRT) concept. Both VSFRT and AI-VIRL use neural networks controllers. We find that AI-VIRL is significantly more computationally demanding and more sensitive to the exploration settings, while leading to inferior LRMO tracking performance when compared to VSFRT. It is not helped either by transfer learning the VSFRT control as initialization for AI-VIRL. State dimensionality reduction using machine learning techniques such as principal component analysis and autoencoders does not improve on the best learned tracking performance however it trades off the learning complexity. Surprisingly, unlike AI-VIRL, the VSFRT control is one-shot (non-iterative) and learns stabilizing controllers even in poorly, open-loop explored environments, proving to be superior in learning LRMO tracking control. Validation on two nonlinear coupled multivariable complex systems serves as a comprehensive case study.

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“…Recently, adaptive dynamic programming (ADP) has been used extensively in different applications to solve different optimal control problems [25–28]. Usually, there are two neural networks in an ADP design.…”

Section: Introductionmentioning

confidence: 99%

Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

Khiabani

Heydari

2020

IET power electron.

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Sub‐optimal switching in anti‐lock brake systems using approximate dynamic programming

Cited by 26 publications

References 57 publications

Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

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