Output-Tracking-Error-Constrained Robust Positioning Control for a Nonsmooth Nonlinear Dynamic System

Han, Seong-Ik; Lee, Jang Myung

doi:10.1109/tie.2014.2316263

Cited by 45 publications

(22 citation statements)

References 19 publications

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“…To overcome the matching condition, several adaptive neural or fuzzy control approaches were designed in [32][33][34][35][36] for nonlinear systems in strict and non-strict feedback with nonlinear dead-zone. Two outstanding adaptive control techniques and applications using fuzzy echo state neural networks were developed in [37,38] for nonlinear dynamic system with dead-zone nonsmooth inputs. The control methods in [30][31][32][33][34][35][36][37][38] were designed for nonlinear continuous-time systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Liu

Gao

Tong

et al. 2016

IEEE Trans. Fuzzy Syst.

376

115

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In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame, and contain unknown functions and non-symmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but also achieves the optimal control performance. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of non-symmetric dead-zone on the control performance and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.Index Terms-Adaptive fuzzy control; reinforcement learning; nonlinear systems; optimal control; dead-zone input.1063-6706 (c)

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Section: Introductionmentioning

confidence: 99%

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Liu

Gao

Tong

et al. 2016

IEEE Trans. Fuzzy Syst.

376

115

View full text Add to dashboard Cite

show abstract

“…Therefore, it implies that the state constraints have to be symmetric. On the other hand, in [36], [37], prescribed performance adaptive neural network control is developed to constrain the error signals within a prescribed region, which is shaped by a performance function. This method may be extended to handle the problem of motion constraints, but it is not straightforward to define the corresponding performance function which is related to the tracking error and is required to be exponentially decaying.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method.

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“…Recently, power assist robots and wearable exoskeletons for decreasing labor power and medical rehabilitation have attracted much attention [1], [2], [3], including lower limp power assist orthoses [4], upper extremity robotic systems [5], [7], and full-body robotic exoskeletons [6]. Since the control of exoskeletons presents a significantly increased complexity over the control of traditional robotic manipulators, some control approaches have been extended [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

Wang

et al. 2015

IEEE Trans. Ind. Electron.

216

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To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers for compensating the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity and payloads. The proposed adaptive fuzzy control with updated parameters mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feed-forward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, the extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance the proposed approaches.

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Output-Tracking-Error-Constrained Robust Positioning Control for a Nonsmooth Nonlinear Dynamic System

Cited by 45 publications

References 19 publications

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

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