Improvement And Application Research On Predictive Functional Control Algorithm

Model predictive control algorithm is mainly applied to the slow varying process control at the beginning stages. It’s more complex than PID control algorithm generally and the amount of online computing relatively large. It’s difficult to achieve better control effect in the real-time control occasion of high requirements. With the enhanced computing power, model predictive control can get better control effect in the fast servo control system. While it not point the form of control input and along with the problem of unclear about control input law. Predictive functional control arising in order to solve this problem, then many scholars working on predictive functional control but mainly concentrated on the single-variable system. In reality, many are nonlinear, fast time-varying, strong coupling multivariable systems. It’s extremely important to study the control method of multivariable system. In addition to parameter tuning has not a unified approach, seeking a simple, easy and reliable tuning algorithm is imperative. This article conducted in-depth study of the predictive functional control on the basis of previous studies. The main results are as follows:(1) Describes the generation, basic principles and current research status of predictive functional control.(2) Improved single-variable predictive functional control algorithm-fractional PID predictive functional control algorithm. This algorithm combined fractional PID with predictive functional control and takes the advantage of the two algorithms. Adjust the parameters of improved predictive control algorithm with particle swarm algorithm. Verify the effectiveness of the algorithm through the generator excitation system.(3) Give a detailed derivation of the multivariable predictive functional control algorithms based on state space equations. Verify the effectiveness of the multi-variable predictive functional control algorithm through numerical simulation.(4) Combines the PI with multivariable predictive functional control algorithm by changing the form of predictive function’s objective function. Come into the improved multivariable predictive control with both advantages of PI and predictive functional control, and derive the expression of control amount. Prove the stability and robustness through the second Lyapunov stability theorem. Finally, numerical simulations show the effectiveness of this new algorithm and make a comparison with multivariable predictive functional control algorithm. The results show that this improved algorithm is more flexible and has certain robustness.(5) Near-space vehicle has characteristics such as strong nonlinearity, strong coupling, fast time-varying etc. It can be considered the nonlinear is not very intense when the aircraft flying in the vicinity of a point. According to the principle of small perturbations linear the non-linear model of aircraft attitude in a given equilibrium point. Then, the spacecraft attitude controllers are designed via using multivariable predictive functional control algorithm and multivariable PI predictive functional control algorithm. Effectiveness of the design of the controller through MATLAB simulation has also been verified.