Adaptive Tracking Control of Cooperative Robot Manipulators With Markovian Switched Couplings

Hu, Bin; Guan, Zhi‐Hong; Lewis, Frank L.; Chen, C. L. Philip

doi:10.1109/tie.2020.2972451

Cited by 54 publications

(19 citation statements)

References 28 publications

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“…The robotic moving parts require an effective and precise control scheme for acquiring tasks, most importantly position tracking [1]. In this regard, the nonlinearity characteristics of the robot, as well as the uncertainties of parameters, are the most key challenges against the operator to adjust the managing unit of robot links [2,3].…”

Section: Introductionmentioning

confidence: 99%

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

et al. 2021

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The nonlinearities of the robotic manipulators and the uncertainties of their parameters represent big challenges against the controller design. Moreover, the tracking of regular and irregular trajectories with fewer overshoots, short settling time, and small steady-state error is the main target for the robotic response. The model predictive control (MPC) is an efficient controller to handle the performance requirements. However, the conventional MPC requires the linearization of the system model. The linearization of the model does not cover all dynamics of the robotic system. Thus, this paper introduces the nonlinear MPC (NLMPC) as a proper control method for the nonlinear systems instead of the conventional MPC. Specifically, this work proposes the use of NLMPC for controlling robotic manipulators. However, the NLMPC gains need proper tuning to attain good performance rather than the conventional methods. The neural network algorithm (NNA) considers a sufficient adaptive intelligent technique that can be utilized for this purpose. The restriction in a local optimum reveals the main issue versus artificial intelligence techniques. This paper suggests a new improvement to reinforce the exploration behavior of the NNA to overcome the local restriction issue. This modification is carried out by utilizing the polynomial mutation as an effective method to promise the exploration manner of the intelligence techniques. The proposed system can estimate all states from only the output to reduce the cost of the required sensors to measure all states. The results confirm the superiority of the proposed systems with the estimator with negligible change in the output response. The proposed modified NNA (MNNA) is evaluated with the main NNA, genetic algorithm-based PID control scheme, besides the cuckoo search algorithm-based PID control scheme from other works. The results confirm the robustness and effectiveness of the suggested MNNA-based NLMPC to track regular and irregular trajectories compared with other techniques.

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Section: Introductionmentioning

confidence: 99%

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

et al. 2021

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“…Consensus tracking 4,5 is a fundamental cooperative behavior, and aims at developing the control protocols for all the followers to tracking the desired trajectories, which has a wide range of applications 6 . In Reference 7, the authors designed an adaptive control framework for the cooperative robot manipulators, such that the tracking errors of position and velocity were practically uniformly exponentially stable. In Reference 8, a distributed adaptive control protocol was proposed for the Euler–Lagrange systems, in which the asymptotical consensus tracking was achieved.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time cooperative control for robotic manipulators with motion constraints using unified transformation function

Liu

Zhang

Wang

et al. 2021

Intl J Robust & Nonlinear

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This article investigates the fixed‐time consensus tracking control problem for multiagent systems composed of uncertain l‐link robotic manipulators with position and velocity constraints. Fuzzy logic systems are employed to model the uncertain dynamic of the robotic manipulators. In order to cope with the asymmetric yet time‐varying constraints, a nonlinear transformation function is introduced. With the help of the nonlinear transformation function, the original constrained manipulator systems are converted into the equivalent systems without constraint. As long as the boundedness of the state variables in the transformed systems is ensured, the constraint conditions of the original systems are not violated. Then, a fixed‐time adaptive fuzzy control scheme is developed. Under the proposed control method, the consensus tracking error can converge into a small neighborhood of origin in fixed‐time, and all the signals in the closed‐loop systems are bounded. Meanwhile, the motion constraints are not violated all the time. Specially, in view of the property of nonlinear transformation function, it is not necessary to change the control structure when the systems are subjected to no state constraint. Finally, a simulation is presented to illustrate the feasibility and effectiveness of implementing the proposed control algorithms.

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“…Hybrid systems widely exist in real life as pointed out in [1], and have received extensive attention in many fields, such as communication networks ( [2,3]), renewable energy systems ( [4,5]), legged locomotion ( [6,7,8]), circuit and power systems ( [9,10,11,12]), ecology ( [13,14]), sampled-data systems/control ( [15,16,17]), humancomputer interaction ( [18,19,20]), multi-agent systems ( [21,22]). A hybrid system contains both continuous and discrete variables, which interact with each other, yielding rich dynamical behaviors different from those in traditional mono-dynamic continuous/discrete systems.…”

Section: Introductionmentioning

confidence: 99%

Controller Design For Hybrid Systems With Nonlinear Sub-Systems Using Common Lyapunov Functions

Fan¹,

Fang²,

Wu³

et al. 2021

Preprint

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Hybrid systems are common in real life and have been studied in many different fields. However, due to the interaction of different sub-systems, a hybrid system is much more complex than a mono-dynamic one, where great challenges are confronted when seeking stable controllers either for linear or nonlinear systems. The traditional design scheme for such a system, namely, to design a controller for each sub-system separately, cannot yield satisfactory performance, since the switches between sub-systems are not specifically considered. In fact, the controllers constructed in this way are usually of poor performance, or even unstable, as a disastrous effect caused by the alternation of sub-systems. In this paper, considering the aforementioned problem, a control scheme is proposed to design suitable controllers for hybrid systems to achieve overall stabilization by taking account of the switching behaviors of the system, as well as its sub-systems carefully. The design scheme consists of two steps, wherein the first step aims to design sub-controllers for different sub-systems, usually with different Lyapunov functions, while a common Lyapunov function candidate is composed in the second step to modify the previously designed sub-controllers correspondingly. Following this design scheme, not only the stability, but also the performance of the closed-loop hybrid systems, is successfully guaranteed. Some simulation results are provided to show the satisfactory performance of the proposed design scheme.

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Adaptive Tracking Control of Cooperative Robot Manipulators With Markovian Switched Couplings

Cited by 54 publications

References 28 publications

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

Fixed‐time cooperative control for robotic manipulators with motion constraints using unified transformation function

Controller Design For Hybrid Systems With Nonlinear Sub-Systems Using Common Lyapunov Functions

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