Neural Network Control Of Pneumatic Position Servo System

Pneumatic position servo system uses air as the medium.Its simple structure,safety and reliability,rapid response and other outstanding advantages have made it widely concerned.From the opening and closing of doors in cars and subways to artificial respirators and pneumatic robots,pneumatic position servo system has a vital position in the field of industrial automation.Due to the compressibility of gas,the nonlinear valve flowing and the friction of the cylinder's influences affects the pneumatic position servo system's performance,which brings a lot of difficulties to establish the pneumatic servo system's accurate model.This brings the traditional control method which relying on the system model many difficulties.In the meantime,the influence of time-varying operating point of the pneumatic system and unknown external disturbances makes the trajectory tracking control of the pneumatic system more complicated.Therefore,how to improve the control performance of the pneumatic position servo system is of great significance for expanding the pneumatic position servo system's application field.With full constraints are considered in the controller designing of the pneumatic position servo system controller,this article takes Festo's pneumatic position servo experimental system as the object,and aims to design a high-performance tracking controller.The controllers are designed as follows:(1)To solve the problem of the unknown system model,this topic uses the neural network's ability to approximate smooth functions to identify the unknown function in the pneumatic system model.The wavelet neural network,the radial basis function neural network(RBFNN)and the fuzzy neural network are in use.Combining the Nussbaum function to solve the unknown control direction of system on the way to deal with system gain,the inaccurate proportional valve zero point is taken as a kind of uncertainty,then through backstepping strategy,the adaptive neural network controller is developed.The introduction of Lyapunov theory ensure the system's stability.The final experimental results show that the pneumatic position servo system can realize the trajectory tracking control of the three reference signals under the control direction unknown.(2)Considering the actual state of the pneumatic system is limited,controller designed with ignoring the system's state constraint may result in equipment damage or expected performance degradation.Therefore,after considering the unknown model,unknown proportional valve zero point,and unknown control direction of the pneumatic position servo system,this paper designs the controller considering state constraint of the system to improve the system tracking accuracy.The controller uses a RBFNN to identify unknown models,unknown disturbances,and unknown valve zero points,utilizes Nussbaum type function to deal with unknown control directions,and employs barrier Lyapunov function(BLF)to handle stability problems with state constraint.Finally,Lyapunov theory and Young's inequality are utilized to prove the controlled system's stability.Experimental results prove the proposed method's effectiveness,and the comparative experimental results show the method's superiority.(3)On the basis of considering the influence of unknown model,unknown valve zero point,unknown disturbance,unknown control direction and state constraints.Input saturation,hysteresis characteristics of the solenoid valve and other characteristics are also considered which general controllers are usually ignoring,then designing many kinds of adaptive neural network controllers.The comparative experimental results show that the controller designed which based on fully considering the nonlinear characteristics and constraints of the system not only can protect the working state of the system but also can obtain better tracking control accuracy.The experimental research shows that the hysteresis characteristic affects the system tracking performance most,input saturation is second,and state constraint is to the third place.The research work experimentally verifies the influence of various nonlinear characteristics and other restrictive conditions,this work is not systematic in the literature that can be found.This paper takes the pneumatic position servo control system as a typical application platform,fully considers the unknown factors and constraints of the actual system,and designs neural network-based trajectory tracking controllers.The controller can consider more constraint conditions and unknown factors without a significant increase in complexity.Laying a good foundation for expanding the application of pneumatic servo system.