Model Predictive Control for Rotary Inverted Pendulum ☆

The article presents a practical approach to implementing traditional Model Predictive Control (MPC) for the rotary inverted pendulum, a nonlinear and unstable system. The study begins by constructing the mathematical model of the pendulum and then applying the predictive controller to this model. The algorithm is tested on an experimental model, the Quanser QUBE-Servo2. The paper highlights the advantages of MPC, such as its ability to incorporate constraints and control nonlinear processes, making it a popular choice in industrial applications. However, it also discusses the challenges of applying MPC, particularly in constructing accurate models and solving complex optimization problems. The control task focuses on maintaining the pendulum in an upright position. The research assesses the effectiveness of MPC with and without uncertainty compensation by analyzing response time, settling time, overshoot, and steady-state error through both simulation and experimentation. The results demonstrate the benefits and limitations of the uncertainty compensation MPC algorithm compared to the traditional MPC controller.