Trajectory tracking control algorithm for pneumatic muscle driving system

Abstract

The invention provides a trajectory tracking control algorithm for a pneumatic muscle driving system. The algorithm comprises the steps that an expected trajectory of the pneumatic muscle driving system is input; a tracking error is determined according to the feedback actual trajectory of the pneumatic muscle driving system; a tracking error derivative is obtained through the tracking error; according to a hierarchical fusion deep fuzzy neural network, a control signal is obtained through combination with the tracking error and the tracking error derivative; the control signal is transmittedto a pneumatic driving joint; and the pneumatic driving joint conducts trajectory tracking according to the control signal. According to the trajectory tracking control algorithm, through a hierarchical fusion deep fuzzy neural network controller without pre-training, the problems in the existing technical scheme are effectively solved, and the trajectory tracking control of the pneumatic muscle driving system is achieved.

Description

technical field [0001] The invention relates to the technical field of trajectory tracking control of a pneumatic driver, in particular to a trajectory tracking control algorithm for a pneumatic muscle drive system. Background technique [0002] The pneumatic muscle drive system controls the joints driven by the pneumatic muscles, and the exoskeleton robot moves stably and accurately according to the desired trajectory. The hysteresis and nonlinear characteristics of the pneumatic drive system have brought great challenges to the control of this type of pneumatic drive system. [0003] For pneumatically driven robot systems, the existing control methods can be divided into two categories: model-free control algorithms and model-based control algorithms. Model-free control algorithms are represented by PID control and fuzzy control. Such algorithms do not need to obtain the dynamic model of the controlled object, and can be designed and put into use based on existing knowled...

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Problems solved by technology

[0005] However, the methods of literature [1] and literature [2] are based on the precise dynamic model of aerodynamic muscles, and the model-based T-S fuzzy control in literature [1] is more precise and robust than the model-free fuzzy control. The design of the sliding mode controller and the disturbance observer in [2] requires an accurate dynamic model of the aerodynamic muscle; while the control algorithms based on neural networks and deep networks in [3] and [4] , extracting the data of the controlled object and pre-training the neural network are all indispensable steps before this type of controller is put into use, which brings great inconvenience to the practical application of the controller

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