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In this paper, the parameter identification and control problem are investigated for a mechanical servo system with LuGre friction. First of all, an intelligent glowworm swarm optimization (GSO) algorithm is developed to identify the friction parameters. Then, by using a finite-time parameter estimate law and nonlinear sliding mode technique, an adaptive nonlinear sliding mode control (NSMC) based on GSO is designed to speed up the parameter convergence and to decrease the overshoot and steady-state time in control process. Finally, comparative simulations are given to show that the proposed parameters identification technique and adaptive NSMC law are both effective with respect to fast convergence speed and high tracking accuracy.

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In this paper, a speed tracking and synchronization control approach is proposed for a multimotor system based on fuzzy active disturbance rejection control (FADRC) and enhanced adjacent coupling scheme. By employing fuzzy logic rules to adjust the coefficients of the extended state observer (ESO), FADRC is presented to guarantee the speed tracking performance and enhance the system robustness against external disturbance and parametric variations. Moreover, an enhanced adjacent coupling synchronization control strategy is proposed to simplify the structure of the speed synchronization controller through introducing coupling coefficients into the conventional adjacent coupling approach. Based on the proposed synchronization control scheme, an adaptive integral sliding mode control (AISMC) is investigated such that the chattering problem in conventional sliding mode control can be weakened by designing an adaptive estimation law of the control gain. Comparative simulations are carried out to prove the superiorities of the proposed method.

A full-order sliding mode control based on a fuzzy extended state observer is proposed to control the uncertain chaos in the permanent magnet synchronous motor. Through a simple coordinate transformation, the chaotic PMSM model is transformed into the Brunovsky canonical form, which is more suitable for the controller design. Based on the fuzzy control theory, a fuzzy extended state observer is developed to estimate the unknown states and uncertainties, and the restriction that all the system states should be completely measurable is avoided. Thereafter, a full-order sliding mode controller is designed to ensure the convergence of all system states without any chattering problem. Comparative simulations show the effectiveness and superior performance of the proposed control method.

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