Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors

Wei, Qinglai; Liu, Derong

doi:10.1016/j.neucom.2013.09.069

Cited by 74 publications

(29 citation statements)

References 42 publications

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“…However, it is difficult to solve the HJB equation (11) to obtain * ( ) due to the uncertain parameters in matrix A and the unknown road displacement input. The usual method is to get the approximate solution via a critic NN as in [12,13]. Hence, from Assumption 2, it is justified to assume that there exist weights 1 such that the value function * ( ) is approximated as…”

Section: Controller Design Based On Approximate Dynamic Programmingmentioning

confidence: 99%

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Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single‐Network Approximate Dynamic Programming

Ning

et al. 2017

Mathematical Problems in Engineering

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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.

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Section: Controller Design Based On Approximate Dynamic Programmingmentioning

confidence: 99%

“…For system (3) with adaptive optimal control signal given in (14), and adaptive law (20), the optimal control converges to a small bound around its ideal optimal solution * in (13).…”

Section: Theoremmentioning

confidence: 99%

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single‐Network Approximate Dynamic Programming

Ning

et al. 2017

Mathematical Problems in Engineering

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“…[41][42][43][44] In this paper, we will use a three-layer MIMO neural network, which can be seen in Fig. 1.…”

Section: B Description Of the Neural Networkmentioning

confidence: 99%

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Jiao

2017

AIP Advances

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In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

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“…9,[46][47][48][49][50][51][52][53] In this paper, we will use a three-layer multiple-input multiple-output (MIMO) neural network, which can be seen in Figure 1.…”

Section: B Description Of the Neural Networkmentioning

confidence: 99%

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

Wu¹,

2016

AIP Advances

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In this paper, we consider the control problem of a class of uncertain fractionalorder chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

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Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors

Cited by 74 publications

References 42 publications

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single‐Network Approximate Dynamic Programming

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single‐Network Approximate Dynamic Programming

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

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