Removing Feasibility Conditions on Force Control for a Compliant Grinding Device With Asymmetric Full-State Constraints and Hysteresis Nonlinearity

A “planning and control” scheme with hysteresis compensation is developed in this article for a compliant grinding device (CGD) driven by a pneumatic actuator with asymmetric full-state constraints. First, in the planning part, we propose an improved hysteresis model and construct its inverse model through the modified inverse multiplicative structure method to deal with the hysteresis nonlinearity between air pressure and contact force in CGD. In the control part, a neural state observer (SO) is developed to observe the rate of change of air pressure by combining a neural network (NN) and an SO. By fusing an NN and a disturbance observer (DO), a neural DO is presented to observe external disturbances. Asymmetric full-state constraints are realized without the feasibility conditions (FCs) of the virtual controller by the fusion of the backstepping design process and nonlinear state-dependent transformations. It is proven that the air pressure closely tracks the planned desired air pressure, as the tracking error of the air pressure is uniformly ultimately bounded. Finally, hardware experiments show that the force control accuracy of the proposed method is within 2N, which achieves a satisfactory force control effect and verifies the effectiveness and robustness of the presented method.