Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation

Li, Yongming; Tong, Shaocheng; Li, Tieshan

doi:10.1109/tfuzz.2015.2486811

Cited by 371 publications

(115 citation statements)

References 56 publications

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“…Before introducing our control design method, let us first recall the approximation property of the NNs [11][12][13][14][36][37][38][39][40][41][42][43][44][45] . The NNs take the form of W T σ ( ξ ) where W ∈ R ×m is called weight matrix, with being the number of NN nodes; and σ (ξ ) ∈ R is a vector valued function defined in R , with ξ ∈ R q being the NN input vector.…”

Section: Neural Networkmentioning

confidence: 99%

“…Many general adaptive neural controllers ensure the SGUUB tracking stability for marine vehicles [1,[13][14][15][27][28][29] , provided that the trajectory stays within the neural active region throughout the control process. However, under the influence of external disturbances or an improper initialization, the tracking performance will be destroyed or even cause the instability of the system.…”

Section: Cooperative Path Following Designmentioning

confidence: 99%

“…In [10] , with the consideration of controllability constraints arising due to underactuation, a nonlinear cooperative path following controller is developed, and that is suitable for both unidirectional and bidirectional communication links. However, a key feature of the aforementioned results [3,[8][9][10] is that the cooperative path following controllers are designed based on a backstepping technique [11][12][13] . The backstepping based cooperative path following controller has an obvious drawback.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed

Wang

Liu

2018

Neurocomputing

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Section: Neural Networkmentioning

confidence: 99%

Section: Cooperative Path Following Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed

Wang

Liu

2018

Neurocomputing

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“…[8,9,[39][40][41][42][43][44]): the knowledge base, the fuzzifier, the fuzzy inference engine working on the fuzzy rules, and the defuzzifier. Usually, a fuzzy logic system is modeled bŷ= …”

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic (Hyperchaotic) Systems with Input Saturation and Unknown Parameters

Liu

Chen

et al. 2017

Complexity

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We investigate the synchronization problem of fractional-order chaotic systems with input saturation and unknown external disturbance by means of adaptive fuzzy control. An adaptive controller, accompanied with fractional adaptation law, is established, fuzzy logic systems are used to approximate the unknown nonlinear functions, and the fractional Lyapunov stability theorem is used to analyze the stability. This control method can realize the synchronization of two fractional-order chaotic or hyperchaotic systems and the synchronization error tends to zero asymptotically. Finally, we show the effectiveness of the proposed method by two simulation examples.

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“…Robust adaptive motion/force tracking control was proposed for uncertain constrained robot manipulators in [59]. In [60], a hybrid fuzzy adaptive output feedback control design was proposed for uncertain MIMO nonlinear systems with time-varying delays and input saturation. In this paper, the adaptive neural output feedback control scheme will be developed by using the RBFNN and disturbance observer for uncertain robot manipulators with input saturation based on backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Mei

Cheng-jiang

2017

Complexity

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This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

show abstract

Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation

Cited by 371 publications

References 56 publications

Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed

Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic (Hyperchaotic) Systems with Input Saturation and Unknown Parameters

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

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