Implementation of model predictive control using Algorithm-3 on Arduino Mega 2560 for speed control of BLDC motor

Abstract

Many physical process systems have performance limitation which limits their performance regardless the input. This limitation usually occurs in the form of actuator input saturation as the constraints of the system. To handle this input constraint, Model Predictive Control (MPC) is used as one of the popular advanced control technique in an industrial process to control a system. MPC contains quadratic objective function having constraints on the variables which must be resolved at each sampling time to produce an optimal control input to the system. This process of optimizing a certain quadratic function is called Quadratic Programming (QP). The computation of QP is usually slow and makes MPC takes a longer time to produce an optimal control input compare to other control technique especially in the online computation for real time embedded application. This problem can cause undesired result when used to control fast dynamic system such as BLDC motors. To overcome this problem, an iterative algorithm of Algorithm-3 is used as a fast QP computation solver. In this paper, MPC will be implemented to control the speed of BLDC motor using Arduino Mega 2560. Before implementation, system modeling, controller designing, and simulation are done using MATLAB. For the sake of comparison, the performance of MPC using Algorithm-3 will be compared with classic control techniques such as PID. From the results, it can be observed that MPC gives better performance than PID and it is shown that MPC capable to handle input saturation of the system better than PID.