Proportional-Integral Approximation-Free Control of Robotic Systems With Unknown Dynamics

Zhang, Chao; Na, Jing; Wu, Jiande; Chen, Qiang; Huang, Yingbo

doi:10.1109/tmech.2020.3035660

Cited by 34 publications

(14 citation statements)

References 30 publications

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“…Theorem 1. For the adopted critic NN with adaptive law (23), if the regressor vector Ξ in (18) satisfies the PE condition, the critic NN weight error 􏽥 W exponentially converges to a small bounded set around zero.…”

Section: Remark 3 E Adaptive Law (23) Is Driven By the Estimation Error 􏽥mentioning

confidence: 99%

“…is section will develop a simulation based on a 2-DOF robot [18,23]. To realize the simulation, the robotic systems model can be defined as…”

Section: Application To Robotic Systemsmentioning

confidence: 99%

“…For system (2) with practical optimal control (15) and adaptive law (23), if the regressor Ξ satisfies the PE condition, the error 􏽥 W converges to a small set around zero. Moreover, the actual optimal control u in (15) converges to a region around its optimal solution u * in ( 14), i.e., ‖u − u * ‖ ≤ ε u .…”

mentioning

confidence: 99%

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Adaptive Critic Learning‐Based Robust Control of Systems with Uncertain Dynamics

Zhao

Zeng

Guo

2021

Computational Intelligence and Neuroscience

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Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first transformed into an optimal control of nominal system via selecting the appropriate cost function. Then, we develop an adaptive critic leaning algorithm to learn online the optimal control solution, where only the critic neural network (NN) is used, and the actor NN widely used in the existing methods is removed. Finally, the feasibility analysis of the control algorithm is given in the paper. Simulation results are given to show the availability of the presented control method.

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Section: Remark 3 E Adaptive Law (23) Is Driven By the Estimation Error 􏽥mentioning

confidence: 99%

“…is section will develop a simulation based on a 2-DOF robot [18,23]. To realize the simulation, the robotic systems model can be defined as…”

Section: Application To Robotic Systemsmentioning

confidence: 99%

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Adaptive Critic Learning‐Based Robust Control of Systems with Uncertain Dynamics

Zhao

Zeng

Guo

2021

Computational Intelligence and Neuroscience

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“…In [29], an approximation-free control (AFC) was developed for nonlinear systems without using any function NN or FLS approximations, and a proportional-like controller was designed with a simplified structure. Owing to this attractive property, the AFC method is easier to implement in various applications [30][31][32][33][34][35]. In [33], an adaptive error-constrained finite-time controller was presented for Lagrangian systems, where a piecewise function should be required in the controller design to handle the singularity problem, which would lead to a complex stability analysis.…”

Section: Introductionmentioning

confidence: 99%

Finite-time command-filtered approximation-free attitude tracking control of rigid spacecraft

Xie

Chen

et al. 2022

Nonlinear Dyn

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This paper presents a finite-time commandfiltered approximation-free attitude tracking control for rigid spacecraft. A novel finite-time prescribed performance function (FTPPF) is first constructed to ensure that the attitude tracking errors converge to the predefined region in finite time. Then, a finite-time error compensation mechanism is constructed and incorporated into the backstepping control design, such that the differentiation of virtual control signals in recursive steps can be avoided to overcome the singularity issue. Compared with most of approximation-based attitude control methods, less computational burden and lower complexity are guaranteed by the proposed approximation-free control scheme due to the avoidance of using any function approximations. Simulation-

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“…PPC has been used in many control engineering applications requiring output constraints [41], [42]. Chao Zhang et al [43] proposed a novel proportional-integral approximation-free control by using PPFs for nonlinear robotic systems without employing any function approximation. To stabilize the vertical and pitch displacements of active suspensions with parametric uncertainties, an adaptive control with PPF constraints was proposed by Jing Na in [44].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Command Filtered Control for Pneumatic Active Suspension With Prescribed Performance and Input Saturation

Tran

Nguyen

et al. 2021

IEEE Access

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In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with the vertical displacement constraint of sprung mass and actuator saturation. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamic theory to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, the prescribed performance function (PPF) is suggested to guarantee that the tracking error of the sprung mass displacement does not violate the constraint boundaries. Based on the command filtered backstepping control with PPF, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, the comparative simulation examples for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed control.INDEX TERMS Active suspension systems (ASSs), neural networks (NNs), command filtered control (CFC), input saturation, prescribed performance function.

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Proportional-Integral Approximation-Free Control of Robotic Systems With Unknown Dynamics

Cited by 34 publications

References 30 publications

Adaptive Critic Learning‐Based Robust Control of Systems with Uncertain Dynamics

Adaptive Critic Learning‐Based Robust Control of Systems with Uncertain Dynamics

Finite-time command-filtered approximation-free attitude tracking control of rigid spacecraft

Adaptive Neural Command Filtered Control for Pneumatic Active Suspension With Prescribed Performance and Input Saturation

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