Review of the Cerebellar Model Articulation Controller

Li, Xin; Gui, Fang; Qiu, Ya

doi:10.23919/ccc55666.2022.9901793

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…It has both stronger generalization ability and faster learning speed than many other neural networks, and it is suitable for approximating complex nonlinear functions. 18,19 Neural networks based output feedback control schemes have been deeply studied in theory and most methods are relatively complex and create large computation burden, which restrict its application in the real-time control filed. 20,21 Thus, most of the current research is in the simulation stage and there are few experimental studies on this method.…”

Section: Introductionmentioning

confidence: 99%

“…CMAC uses its unique addressing method to complete nonlinear spatial mapping. It has both stronger generalization ability and faster learning speed than many other neural networks, and it is suitable for approximating complex nonlinear functions 18,19 . Neural networks based output feedback control schemes have been deeply studied in theory and most methods are relatively complex and create large computation burden, which restrict its application in the real‐time control filed 20,21 .…”

Section: Introductionmentioning

confidence: 99%

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Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance

Hu,

Cao,

Bai

et al. 2024

Adaptive Control & Signal

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SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate the system parameter estimation error and other uncertain nonlinearity to improve the performance of the traditional observer. A robust adaptive controller and another neural network are combined to decrease the impact of parameter perturbation and external disturbance and strengthen the system robustness. Lyapunov theorem is used to derive an on‐line weight adaption law for the two neural networks and an on‐line parameter adaptation law for the uncertain parameters to stabilize the system. Thus the parameter uncertainty and disturbance can be compensated with feedforward compensation technique. A nonlinear robust feedback term is designed to suppress the model compensation deviation. Considering the stronger approximation ability of CMAC basis function against RBF basis function, an improved CMAC neural network is introduced in the proposed controller. A large number of simulations and experiments show that the proposed controller has better tracking performance than many main‐stream output feedback controllers in present.

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