Integral reinforcement learning based decentralized optimal tracking control of unknown nonlinear large-scale interconnected systems with constrained-input

Liu, Chong; Zhang, Huaguang; Xiao, Geyang; Sun, Shaoxin

doi:10.1016/j.neucom.2018.09.011

Cited by 56 publications

(23 citation statements)

References 42 publications

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“…The optimal tracking control of large-scale networked system is developed by using integral reinforcement learning method. 6 The fast converging problem of the consensus of networked high-order agents is investigated in ref. [4], where the authors show the possibility of augmenting the consensus speed by designing a linear control input of the leader.…”

Section: Introductionmentioning

confidence: 99%

Fast Convergence of Multi-quadrotor Cooperation Using Weighted-Neighbor-based Control

Hou

Zhang²

2021

Robotica

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SUMMARY In this paper, a weighted-neighbor-based cooperation control of multi-quadrotor systems is investigated. A formation tracking problem is treated, where the reference formation trajectory (RFT) is not given a priori. The RFT is only available to some of the quadrotors (i.e. the leaders). In order to attain the fast convergence of the agents, we propose an algorithm to calculate the neighbors’ weights in decentralized way. Then, the weights are used to compose the formation controller. Compared to the widely used average-neighbor-based control method, the proposed control protocol can increase the convergence speed of the cooperation error. Since the formation control is improved in topological scale, the utilization of the proposed algorithms can be extended on any multi-robot systems. We show the improvement of the proposed control protocol by theoretical proof, simulation, and real-time experiments.

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Section: Introductionmentioning

confidence: 99%

Fast Convergence of Multi-quadrotor Cooperation Using Weighted-Neighbor-based Control

Hou

Zhang²

2021

Robotica

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“…Large‐scale system is a kind of system with main features such as high dimensionality, complexity, and constraints on the information flow for modeling, analyzing, and controlling the system. Stability and the control problem for these systems, especially in the presence of different faults, have always been regarded as a challenging issue 34‐46 . Chen and Tao 34 proposed an adaptive neural control scheme by using backstepping technique for a class of nonlinear large‐scale systems in the presence of external disturbances and actuator faults.…”

Section: Introductionmentioning

confidence: 99%

“…Sui et al 45 proposed a finite time adaptive fuzzy decentralized control approach for nonlinear nonstrict feedback large‐scale systems with immeasurable states. Liu et al 46 presented a decentralized optimal tracking control problem for nonlinear large‐scale systems with constrained input. The considered large‐scale systems were transformed to some nominal isolated subsystems, then tracking problem was solved by utilizing integral reinforcement learning method.…”

Section: Introductionmentioning

confidence: 99%

“…Stability and the control problem for these systems, especially in the presence of different faults, have always been regarded as a challenging issue. [34][35][36][37][38][39][40][41][42][43][44][45][46] Chen and Tao 34 proposed an adaptive neural control scheme by using backstepping technique for a class of nonlinear large-scale systems in the presence of external disturbances and actuator faults. The mentioned disturbance observer was designed to estimate the external disturbances and neural network approximation errors by utilizing an auxiliary system.…”

Section: Introductionmentioning

confidence: 99%

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Design of disturbance observer based on adaptive‐neural control for large‐scale time‐delay systems in the presence of actuator fault and unknown dead zone

Janbazi

Hashemi

2020

Adaptive Control & Signal

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This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov-Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.

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“…The strategy for each agent is selected using an iterative dynamic programming approach in order to insure that, the agent is moving within a convex hull of leaders which in turn guarantees Nash equilibrium. A decentralised approach based on IRL is employed to solve a large scale control problem of interconnected systems with constrained inputs in [37]. The interconnected systems are divided into sub nominal systems where each tracking problem is solved using IRL control mechanism individually.…”

Section: Introductionmentioning

confidence: 99%

Integral reinforcement learning solutions for a synchronisation system with constrained policies

Abouheaf

Mahmoud

Gueaieb

2020

IET Control Theory & Applications

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Integral reinforcement learning based decentralized optimal tracking control of unknown nonlinear large-scale interconnected systems with constrained-input

Cited by 56 publications

References 42 publications

Fast Convergence of Multi-quadrotor Cooperation Using Weighted-Neighbor-based Control

Fast Convergence of Multi-quadrotor Cooperation Using Weighted-Neighbor-based Control

Design of disturbance observer based on adaptive‐neural control for large‐scale time‐delay systems in the presence of actuator fault and unknown dead zone

Integral reinforcement learning solutions for a synchronisation system with constrained policies

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