Reference tracking of nonlinear airborne systems using stochastic MPC with disturbance observers and actuator chance constraint optimization

Abstract

With ever-growing advancements in the field of aerospace technology, there are much wider opportunities and capabilities emerging as a constant demand in terms of speed and accuracy. One of the major segments for any airborne mission is its Guidance-Navigation-Control technique which becomes very crucial for the aspects of speed and accuracy, especially for defense or highly complex missions like missile launches, space missions, airstrikes, and many more. A controller technique that is much more accurate than its traditional counterparts like Proportional-Integral-Derivative (PID) Control and Linear Quadratic Regulator (LQR), as well as constantly considering any instantaneous state measurement noises into account, is an irresistible demand and advantageous over other control techniques in use. A Model Predictive Controller (MPC) can smoothly handle actuator constraints, angle-of-attack constraints, and pitch-angle constraints which makes it more versatile and near perfect for practical nonlinear systems. A real-world nonlinear airborne system is mathematically modeled and represented in its nonlinear state space representation. To accurately measure the instantaneous states, the Kalman filter with distinct covariances for both state and control measurements is taken considering noises to be normally distributed. This is followed by the nonlinear model predictive controller which acts based on the system’s guidance requirements on the perturbated states at each time instance for optimal reference path tracking as well as dealing with stochastic actuator values optimized by chance-constraint programming. The performance of the proposed reference tracking technique is implemented in MATLAB/Simulink tool with numerical as well as graphical analysis results.