Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis

2019 ◽  
Vol 17 (7) ◽  
pp. 1642-1654 ◽  
Author(s):  
Li Tang ◽  
Dongjuan Li
Keyword(s):  
Lyapunov Function ◽  
Controller Design ◽  
Time Varying ◽  
Feedback Systems ◽  
Neural Controller ◽  
Barrier Lyapunov Function ◽  
Adaptive Neural Controller

Tracking Control for Switched Nonlinear Systems Subject to Time-Varying Output Constraints

2012 ◽  
Author(s):  
Ben Niu ◽  
Georgi M. Dimirovski ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Closed Loop ◽  
Time Varying ◽  
Switched Nonlinear Systems ◽  
Feedback Form ◽  
Barrier Lyapunov Function ◽  
Output Constraints ◽  
Asymptotic Tracking

In this paper, we address the tracking control problem for switched nonlinear systems in strict-feedback form with time-varying output constraints. To prevent the output from violating the time-varying constraints, we employ a Barrier Lyapunov Function, which relies explicitly on time. Based on the simultaneous domination assumption, we design a controller for the switched system, which guarantees that asymptotic tracking is achieved without transgression of the constraints and all closed-loop signals remain bounded under arbitrary switchings. The effectiveness of the proposed results is illustrated using a numerical example.

Stability Analysis and Controller Design for Fuzzy Parameter Varying Systems Based on Fuzzy Lyapunov Function

2018 ◽  
Author(s):  
Xiaojun Ban ◽  
Hongyang Zhang ◽  
Fen Wu
Keyword(s):  
Lyapunov Function ◽  
Controller Design ◽  
Design Procedure ◽  
Handling Time ◽  
Feedback Gain ◽  
Time Varying ◽  
System A ◽  
Fuzzy Lyapunov Function ◽  
Parameter Varying ◽  
Fuzzy Parameter

The fuzzy parameter varying (FPV) system is a mathematical model proposed to handle nonlinear time-varying dynamical systems encountered in engineering, which has some essential advantages in handling time-varying models. In this article, a new relaxation approach is proposed for the analysis and controller design of the FPV system. Different from the current results on the FPV system, the proposed approach employs the fuzzy Lyapunov function and full block S-procedure to reduce the conservatism in analysis. Furthermore, the relaxation technique proposed in this article can be also used in solving controller synthesis problem effectively. As a result, a design procedure of non-PDC output feedback gain-scheduling controller is provided to ensure asymptotic stability of the closed-loop FPV system. A numerical example is provided to illustrate the proposed method.

LEARNING TO GENERATE ARTIFICIAL FOVEA TRAJECTORIES FOR TARGET DETECTION

1991 ◽  
Vol 02 (01n02) ◽  
pp. 125-134 ◽  
Author(s):  
Jürgen Schmidhuber ◽  
Rudolf Huber
Keyword(s):  
Pattern Recognition ◽  
Eye Movements ◽  
Target Detection ◽  
Time Varying ◽  
Adaptive Model ◽  
Moving Targets ◽  
Credit Assignment ◽  
Neural Controller ◽  
Adaptive Neural Controller ◽  
Sequential Generation

This paper shows how ‘static’ neural approaches to adaptive target detection can be replaced by a more efficient and more sequential alternative. The latter is inspired by the observation that biological systems employ sequential eye movements for pattern recognition. A system is described, which builds an adaptive model of the time-varying inputs of an artificial fovea controlled by an adaptive neural controller. The controller uses the adaptive model for learning the sequential generation of fovea trajectories causing the fovea to move to a target in a visual scene. The system also learns to track moving targets. No teacher provides the desired activations of ‘eye muscles’ at various times. The only goal information is the shape of the target. Since the task is a ‘reward-only-at-goal’ task, it involves a complex temporal credit assignment problem. Some implications for adaptive attentive systems in general are discussed.

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