Command Filter Tracking Control Of Asynchronous Motor Based On State Constraint

Induction motor(IM)has been widely used in robotics,aerospace and other industrial fields because of its reliable performance,simple structure and low cost.However,because the IM dynamic model is highly nonlinear and multivariable,it is a difficult task to obtain good control performance.In addition,in the tracking control of IM,the inherent characteristics of the motor limit the state variables such as current,speed and rotor angular speed.If the status or output of IM exceeds the limit during operation,the performance of the system may be reduced or damaged,and even lead to failure and safety problems.At the same time,it is very important to improve the rapidity and anti-interference performance of the system and meet higher control requirements.Therefore,it is urgent to study a fast and efficient control method for IM system considering state constraints.In this paper,an induction motor command filtering tracking control strategy based on state constraints is proposed by combining command filtered backstepping,barrier Lyapunov function,neural networks,finite-time control and observer technology.The main research results are as follows:For uncertain nonlinear constrained systems,a new finite-time control strategy based on command filter approximation is studied.Firstly,the proposed command filter and a neural network are used to deal with the unknown nonlinearity and external interference in the system.In addition,the combination of barrier Lyapunov function and finite-time theory not only ensures that the system constraints are not violated,but also ensures finite time convergence.For IM drive system,a new adaptive neural network command filtered control strategy based on state constraints and reduced order observer is studied on the basis of considering state constraints.Firstly,the barrier Lyapunov function is employed to guarantee that the rotor position of IM drive system runs in a given range.Then,the adaptive neural technology is used to deal with unknown parameters and load disturbance,and the reducedorder observer is built to evaluate the rotor angular velocity of the IM drive system.Finally,the command filtered technology is used to deal with the problem of "explosion of complexity",and the error compensation mechanism is combined with the command filtered backstepping technology to deal with the filtering error of the system.For IM drive system,considering iron losses and state constraints,a new adaptive finite-time control strategy based on dynamic surface control is studied.Firstly,considering the influence of iron losses on IM,the tracking performance of the system is improved.In addition,the dynamic surface technology and finite-time control technology are combined to solve the problem of " explosion of complexity",speed up the response speed of the system and realize fast and effective tracking.A command filter approximation-based finite-time neural network control scheme is proposed for the tracking control of IM drive system with full state constraints.Firstly,command filters and neural networks are used to reconstruct the approximate value of unknown nonlinearities in IM drive systems,and convex optimization technology is applied to construct the update law of the weights of the neural networks.In addition,the barrier Lyapunov function and finite-time control technology are introduced to ensure that the system state variables are within the given limit range,accelerate the system response speed and realize fast and effective tracking.