SummaryA kind of launching platform driven by two permanent magnet synchronous motors which is used to launch kinetic load to hit the target always faces strong parameter uncertainties and strong external disturbance such as the air current impulsion which would degrade their tracking accuracy greatly. In this paper, a practical method which combines adaptive robust control with neural network‐based disturbance observer is proposed for high‐accuracy motion control of the launching platform. The proposed controller not only accounts for the parametric uncertainties but also takes the external disturbances into account. Adaptive control is designed to compensate the former, while neural network‐based disturbance observer is designed to compensate the latter respectively and both of them are integrated together via a feedforward cancellation technique. A new kind of parametric adaptation and weight adaptation strategy is designed by using the linear combination of the system's tracking error and the weight estimation error as a driving signal for parametric adaptation and disturbance approximation. The stability of the novel control scheme is analyzed via a Lyapunov method and this method presents a prescribed output tracking performance in the presence of both parameter uncertainties and unmodeled nonlinearities. Extensive comparative simulation and experimental results are obtained to verify the high‐performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

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In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

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