Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems

Liu, Yan Jun; Lu, Shumin; Li, Dong-Juan; Tong, Shaocheng

doi:10.1109/tsmc.2016.2633007

Cited by 252 publications

(115 citation statements)

References 66 publications

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“…Lemma 3 (see [54]). For any time-varying function ( ), the following inequality holds for all error function ( ) in the interval | ( )| ≤ ( ):…”

Section: Complexitymentioning

confidence: 99%

“…The authors considering time-delay condition investigate the robotic manipulator system [47], nonlinear MIMO unknown systems [48], and common nonlinear systems [49] with full state constrains [50]. The authors further research the nonlinear pure-feedback systems, Nussbaum gain adaptive control, and ABLF adaptive controller to apply them on stochastic nonlinear systems and wheeled mobile robotic system by using the theory of integral barrier Lyapunov function or state constrains in [51], [52], [53], [54], [55], and [56], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

2017

Complexity

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An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

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“…Lemma 3 (see [54]). For any time-varying function ( ), the following inequality holds for all error function ( ) in the interval | ( )| ≤ ( ):…”

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

2017

Complexity

Self Cite

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“…From (2), the states of DC systems are constrained by the considered time-varying functions. In [35,36], the constraint problem is omitted, which is the main factor of the oscillations of the engineering systems. The authors in [39] addressed the stability problem of DC motor systems with constant constraint which is the special case of the time-varying constraint.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…As we known, the constant constraint is the special case of the time-varying constraint. Subsequently, the authors in [35,36] proposed some adaptive control approaches to address the stability problem of nonlinear systems with timevarying constraints.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

2018

Complexity

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This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with timevarying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

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“…For uncertain systems with states bounded constraints, an adaptive NN control strategy was developed by uniting novel integral barrier Lyapunov functions (BLFs) in the work of Liu et al The full‐state constraints for a kind of stochastic nonlinear systems and all the states under the function of controller can be ensured in their constraint bounds without being destroyed in the other work of Liu et al An adaptive NN tracking controller combining with RBF was designed for a kind of marine surface vessels with unknown parameters and full state constraints in the work of Yin et al Howbeit, the state constraints in these mentioned literary works only focused on the invariability of compact sets, and the result is that these approaches will become invalid when the constraints are defined in some variable domain sets. In the work of Liu et al, the full states were constrained in time‐varying regions, and an adaptive controller was addressed for a class of nonlinear strict‐feedback systems. It is necessary to be pointed out that the constraints were mainly researched on the states of closed‐loop systems in other works .…”

Section: Introductionmentioning

confidence: 99%

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

Fan

Kang

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.

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Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems

Cited by 252 publications

References 66 publications

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

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