scholarly journals Undiscounted Control Policy Generation for Continuous-Valued Optimal Control by Approximate Dynamic Programming

2021 ◽  
pp. 1-35
Author(s):  
Jonathan Lock ◽  
Tomas McKelvey
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Control Policy

scholarly journals Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhi-Jun Fu ◽  
Bin Li ◽  
Xiao-Bin Ning ◽  
Wei-Dong Xie
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Control Method ◽  
Active Suspension ◽  
Suspension System ◽  
Lyapunov Theory ◽  
Adaptive Optimal Control ◽  
Suspension Systems ◽  
Optimal Control Method

In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation) for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP). Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR) approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass) and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.

scholarly journals Sub-Optimal Control of Autonomous Wheel Loader With Approximate Dynamic Programming

2019 ◽  
Author(s):  
Tohid Sardarmehni ◽  
Xingyong Song
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
A Priori ◽  
Optimal Path ◽  
Mean Value ◽  
Wheel Loader ◽  
Engine Model ◽  
Control Objective ◽  
Wheel Loaders

Abstract Optimal control of wheel loaders in short loading cycles is studied in this paper. For modeling the wheel loader, the data from a validated diesel engine model is used to find a control oriented mean value engine model. The driveline is modeled as a switched system with three constant gear ratios (modes) of −60 for backwarding, 60 for forwarding, and zero for stopping. With these three modes, the sequence of active modes in a short loading cycle is fixed as backwarding, stopping, forwarding, and stopping. For the control part, it is assumed that the optimal path is known a priori. Given the mode sequence, the control objective is finding the optimal switching time instants between the modes while the wheel loader tracks the optimal path. To solve the optimal control problem, approximate dynamic programming is used. Simulation results are provided to show the effectiveness of the solution.

Continuous Optimal Infeed Control for Cylindrical Plunge Grinding, Part 1: Methodology

2004 ◽  
Vol 126 (2) ◽  
pp. 327-333 ◽  
Author(s):  
Shaoqiang Dong ◽  
Kourosh Danai ◽  
Stephen Malkin ◽  
Abhijit Deshmukh
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Cycle Time ◽  
Optimal Policy ◽  
Controller Design ◽  
Control Policy ◽  
Active Constraint ◽  
Plunge Grinding ◽  
On Line ◽  
Sampling Instant

A new methodology is developed for optimal infeed control of cylindrical plunge grinding cycles. Unlike conventional cycles having a few sequential stages with discrete infeed rates, the new methodology allows for continuous variation of the infeed rate to further reduce the cycle time. Distinctive characteristics of optimal grinding cycles with variable infeed rates were investigated by applying dynamic programming to a simulation of the grinding cycle. The simulated optimal cycles were found to consist of distinct segments with predominant constraints. This provided the basis for an optimal control policy whereby the infeed rate is determined according to the active constraint at each segment of the cycle. Accordingly, the controller is designed to identify the state of the cycle at each sampling instant from on-line measurements of power and size, and to then compute the infeed rate according to the optimal policy associated with that state. The optimization policy is described in this paper, and the controller design and its implementation are presented in the following paper [1].

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