Analysis on the developmental trend of adaptive control theory and the applications on electric machine control

Tian, Hongfei; Wang, Yinan; Zhou, Peiyao

doi:10.1109/iccci.2017.8117802

Cited by 1 publication

(1 citation statement)

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“…Thus, it is not easy for us to achieve accurate control with this method. In order to solve the problem of parameter uncertainty in the system, adaptive control has been proposed, 6,7 but it can't deal with the unmodeled uncertainty in the system. In order to solve the problem of high‐accuracy motion control in the system with both parameter uncertainty and uncertain nonlinearity, an adaptive robust control method combining adaptive control and robust control was proposed, 8 and many other control methods can be integrated with this method 9,10 .…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%