Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation

Li, Yongming; Tong, Shaocheng; Li, Tieshan

doi:10.1016/j.fss.2013.11.006

Cited by 244 publications

(136 citation statements)

References 34 publications

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“…Noting the condition (44) and applying the similar backstepping step in Section 3, we have the following results.…”

Section: Static Controller Design With Knowledge Utilization By Invomentioning

confidence: 96%

“…The predefined performance issue is an extremely challenging problem. Recently, adaptive neural prescribed performance controller was proposed in [43,44] for feedback linearizable nonlinear systems by means of transformation functions. The proposed method was also developed to deal with the constrained output tracking control problem in many applications such as robotic systems [45], nonlinear servo mechanisms [46], marine surface vessels [33], nonlinear stochastic large-scale systems with actuator faults [47], and switched nonlinear systems [48].…”

Section: Introductionmentioning

confidence: 99%

“…To solve partial tracking error constraints, a fuzzy dynamic surface control design was developed in [49,50] for a class of strict-feedback nonlinear systems by transforming the state tracking errors into new virtual variables. However, the existing control schemes, such as [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], can only guarantee the stability of closed-loop systems with different constraints, which are not capable of achieving the learning of unknown system dynamics. The main reason is that the derived closed-loop error system is extremely complex, such that its exponential convergence is difficult to be verified using the existing stability analysis tools.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Wang

Zhang

2017

Complexity

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A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steadystate control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

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“…Noting the condition (44) and applying the similar backstepping step in Section 3, we have the following results.…”

Section: Static Controller Design With Knowledge Utilization By Invomentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Wang

Zhang

2017

Complexity

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“…At last some refinements mainly dealing with approximation-based adaptive backstepping control were also derived, for example MIMO systems with unknown coefficient gain signs [115], MIMO time-delay systems with nonlinearities on the inputs [116], with dead-zone [117] and recently MIMO systems with input saturations and output constraints [118].…”

Section: Adaptive Controlmentioning

confidence: 99%

Fuzzy control turns 50: 10 years later

Guerra

Sala

Tanaka

2015

Fuzzy Sets and Systems

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In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.

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“…Because dynamics of pipes conveying fluid are typical nonlinear dynamic behaviours, how to reduce vibration of pipeline has remained one of intense interest to researchers in this field nowadays. At present, the main control methods are PID control, independent modal space control (IMSC) [1,2], linear quadratic optimal control (LQOC) [3], adaptive control [4,5], robust control [6] and fu zzy control [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Research for Adaptive Control of the Pipes Conveying Fluid

Chen¹,

Huang²,

Zhao³

2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract-Pipes with supported ends conveying pulsating fluid may lose the dynamic stability due to parametric instability, therefore the piezoelectric actuators are taken as the control actuators here for controlling vibration of pipeline. First, the controlled mathematical models of pipes conveying fluid were established. Second, model reference adaptive control (MRAC) system and MRAC system based on adapti ve gain of the PD were designed. Third, the effects of controllers were verified and the effects of the control parameters on the controllers were analyzed also. During simulation, the time of vibration displacement decaying to zero, also the time and total energy of control input were taken as evaluation indexes. The results of numerical simulation showed that the pipeline vibration could be controlled well by the controllers which were designed by adapti ve control strategy. Keywords-pipes conveying fluid; model reference adaptive control (MRAC); piezoelectric actuator I.INTRODUCTION Pipes conveying fluid have been widely used in many fields of industry. Considering the fluid is pulsatile, pipeline system may lose the stability by parametric resonances, which the pipeline vibration is continuous and the displacement is very large. Because dynamics of pipes conveying fluid are typical nonlinear dynamic behaviours, how to reduce vibration of pipeline has remained one of intense interest to researchers in this field nowadays. At present, the main control methods are PID control, independent modal space control (IMSC) [1,2], linear quadratic optimal control (LQOC) [3], adaptive control [4,5], robust control [6] and fu zzy control [7], etc.

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Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation

Cited by 244 publications

References 34 publications

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Fuzzy control turns 50: 10 years later

Research for Adaptive Control of the Pipes Conveying Fluid

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