Computing the Autopilot Control Algorithm Using Predictive Functional Control for Unstable Model

Abstract

This paper discusses the computing development of a control algorithm using Predictive Functional Control (PFC) for model-based that having one or more unstable poles. One basic Ballistic Missile model [10] is used as an unstable model to formulate the control law algorithm using PFC. PFC algorithm development is computationally simple as a controller and it is not very complicated as the function of a missile will explode as it reaches the target. Furthermore, the analysis and issues of the implementation relating linear discrete-time unstable process are also being discussed. Hence, designed PFC algorithm need to find the suitable tuning parameters as its play an important part of the designing the autopilot controller. Thus, the tuning of the desired time constant, Ψ and small coincidence horizon n 1 in a single coincidence point shows that the PFC control law is built better in the dynamic pole of the unstable missile mode. As a result, by using a trajectory set-point, some positive results is presented and discussed as the missile follow its reference trajectory via some simulation using MATLAB 7.0.

Enhanced predictive functional control based on equivalent input disturbance and generalized extended state observer for nonlinear systems with fast dynamics

“…2,3 Thereby, offloading the computational burden is always a significant modification trend for the original MPC. Different from the solving an optimization problem by online iteration for full MPC, predictive functional control (PFC), as the third generation of MPC, gives a simple analytic optimal solution, which greatly offloads the online computational burden 4 and makes it possible to adopt MPC in applications with fast dynamics. In addition, PFC proposed by Richalet 5 inherits the overwhelming majority of merits of full MPC, such as precise tracking, 6 robust stabilization.…”