Model-free Adaptive Dynamic Programming Based Near-optimal Decentralized Tracking Control of Reconfigurable Manipulators

Zhao, Bo; Li, Yuanchun

doi:10.1007/s12555-016-0711-5

Cited by 51 publications

(11 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using (11) and (12), the approximated optimal control strategy and positive definition function are obtained…”

Section: The Aperiodic Critic Nns Designmentioning

confidence: 99%

“…The ADP technique has been applied successfully to strict-feedback non-linear systems [5,6], discrete-time non-linear systems [7,8] and nonlinear switched systems [9,10]. Very recently, the ADP technique was extended to derive the decentralised tracking controller for non-linear interconnected systems [11,12]. [11] used an observer-critic structure-based to reconstruct unknown system dynamics and solve the coupled Hamilton-Jacobi-Bellman (HJB) equation, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite‐horizon optimal tracking control for constrained‐input nonlinear interconnected system using aperiodic distributed nonzero‐sum games

Duan

Liu

2021

IET Control Theory & Appl

View full text Add to dashboard Cite

This paper proposes a distributed adaptive dynamic programming scheme to investigate the optimal tracking control problem for finite-horizon non-linear interconnected systems with constraint inputs under aperiodic sampling. A N-player nonzero-sum differential game system is constructed with the presented non-linear interconnected system and the tracking error system by introducing the augment vectors. To address the problems of constrained-input and finite-horizon control, a non-quadratic utility function and a finitehorizon cost function are utilized which will arise in the time-varying Hamilton-Jacobi (HJ) equation. Then, a periodic event-triggered scheme is designed to realize aperiodic sampling, where the consumption of communication resources is reduced and the Zeno behavior is avoided. Under the designed periodic event-triggered scheme, the time-varying HJ equation is almost impossible to get an analytical solution due to its hybrid properties and non-linearity. Therefore, the critic neural networks are used to estimate the optimal solution of the HJ equation, and the weight update law is constructed to guarantee the uniformly ultimate bounded of approximated errors. Further, the hybrid nonzero-sum differential game is confirmed to be uniformly ultimate bounded by using the Lyapunov theory. Finally, the obtained distributed PET control strategy is successfully applied to dispose the missile-target intercepter problem. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Using (11) and (12), the approximated optimal control strategy and positive definition function are obtained…”

Section: The Aperiodic Critic Nns Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite‐horizon optimal tracking control for constrained‐input nonlinear interconnected system using aperiodic distributed nonzero‐sum games

Duan

Liu

2021

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…According to [38,45], the HJBE (8) can be solved by time-triggered ADP algorithm. However, as mentioned in [46], the time-triggered optimal control strategies do not only suffer from heavy computational burden and communication, but also waste limited energy resource.…”

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

“…2. Unlike existing time-triggered control methods [37,38] which ignored the conservation of limited energy resource, in this paper, a novel compensator-critic structure-based event-triggered decentralized tracking control method for MRMs is proposed. It does not only make the actual trajectory of each joint module follow its desired one, but also reduce the computational burden, save the communication, and energy consumption simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

This paper presents a novel compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators (MRMs). On the basis of subsystem dynamics under joint torque feedback (JTF) technique, the proposed tracking error fusion function, which includes position error and velocity error, is utilized to construct performance index function. By analyzing the dynamic uncertainties, a local dynamic information-based robust controller is designed to engage the model uncertainty compensation. Based on adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized tracking control is obtained by solving the event-triggered Hamilton–Jacobi–Bellman equation (HJBE) with the critic neural network (NN). The tracking error of the closed-loop manipulators system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.

show abstract

“…Reinforcement learning (RL) is a popular algorithm for determining a policy that optimizes a user‐defined cost function through interactions with an environment 1‐7 . There are several applications of RL in chemical process engineering, such as a model‐free learning controller for pH control, 8 a policy gradient approach for batch bioprocess systems, 9 and approximate dynamic programming (ADP) for control of fed‐batch bioreactors, 10 control of proppant concentrations inside a fracture for hydraulic fracturing, 11 and control of alkali‐surfactant‐polymer flooding for oil recovery 12 .…”

Section: Introductionmentioning

confidence: 99%

Model‐based reinforcement learning for nonlinear optimal control with practical asymptotic stability guarantees

Kim

Lee

2020

AIChE Journal

View full text Add to dashboard Cite

We propose a new reinforcement learning approach for nonlinear optimal control where the value function is updated as restricted to control Lyapunov function (CLF) and the policy is improved using a variation of Sontag's formula. The practical asymptotic stability of the closed‐loop system is guaranteed during the training and at the end of training without requiring an additional actor network and its update rule. For a single‐layer neural network (NN) with exact basis functions, the approximate function converges to the optimal value function, resulting in the optimal controller. When a deep NN is used, the level set shapes of the trained NN become similar to those of the optimal value function. Because Sontag's formula with CLF is equivalent to the optimal controller when the given CLF has the same level set shapes as the optimal value function, Sontag's formula with the trained NN provides a nearly optimal controller.

show abstract

Model-free Adaptive Dynamic Programming Based Near-optimal Decentralized Tracking Control of Reconfigurable Manipulators

Cited by 51 publications

References 56 publications

Finite‐horizon optimal tracking control for constrained‐input nonlinear interconnected system using aperiodic distributed nonzero‐sum games

Finite‐horizon optimal tracking control for constrained‐input nonlinear interconnected system using aperiodic distributed nonzero‐sum games

Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

Model‐based reinforcement learning for nonlinear optimal control with practical asymptotic stability guarantees

Contact Info

Product

Resources

About