Backstepping Control of Discrete-Time Nonlinear System Under Unknown Dead-zone Constraint

Deolia, Vinay Kumar; Purwar, Shubhi; Sharma, T.N.

doi:10.1109/csnt.2011.78

Cited by 9 publications

(10 citation statements)

References 18 publications

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“…For nonlinear discrete-time systems with dead-zone, Campos and Lewis in [39] developed a dead-zone compensation using the adaptive fuzzy systems. Adaptive NN backstepping control design problem was addressed in [40,41] for nonlinear discrete-time strict feedback systems with unknown dead-zone inputs. In [42], an adaptive fuzzy design was investigated for nonlinear discrete-time pure feedback systems with unknown backlash input and external disturbance.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…It is for the first time to solve this problem. 2) Different from the previous backstepping procedures [12,13,[40][41][42][43], to achieve optimal control objective, from…”

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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show abstract

“…Generally, the dead-zone parameters are unknown and time-variant, which cause challenging problem for the control system. Dead-zone commonly affects all practical systems, such as Mathematical Problems in Engineering mechanics and electronics, so the study on dead-zone has drawn much interest in the control community for a long time [7][8][9][10][11][12]. To solve the problem brought by the deadzone, adaptive dead-zone inverses were proposed in [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

Xia

Shao

Zhao

et al. 2013

Mathematical Problems in Engineering

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This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

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“…Generally, the dead-zone parameters are unknown and time variant, which cause challenging problems for the control system. The dead-zone commonly affects all practical systems, such as mechanical, electrical, electronics etc., so studies on the dead-zone have been drawn much interests in the control community for a long time [5], [6], [7], [8], [9] and [10]. To solve the problems brought by the dead-zone, adaptive dead-zone inverses were proposed in [5], [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [5] built a continuous dead-zone inverse for linear systems with unmeasurable dead-zone outputs, while an asymptotical adaptive cancellation of an unknown dead-zone was achieved analytically under the condition that the output of the dead-zone was measurable in [6]. For a general nonlinear actuator dead-zone of unknown width, a compensation scheme using neural networks was presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control with backstepping for surface ships

Xia¹,

Shao²

2013

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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This paper addressed the problem of adaptive fuzzy logic systems with backstepping for surface vessels with input dead-zone. The combination of approximation-based adaptive techniques and fuzzy logic systems is used for approximating the nonlinear function from the ship plant. Through backstepping and Lyapunov synthesis, an indirect adaptive fuzzy logic controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using the fuzzy logic to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearities are well compensated. A set of simulations are carried out to verify the performance of the proposed controller.

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Backstepping Control of Discrete-Time Nonlinear System Under Unknown Dead-zone Constraint

Cited by 9 publications

References 18 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 Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

Adaptive fuzzy control with backstepping for surface ships

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