Model Predictive Control for Rotary Inverted Pendulum

The article presents a practical approach for implementing traditional Model Predictive Control (MPC) on a rotary inverted pendulum, a highly nonlinear and inherently unstable system. The study begins with the development of a mathematical model of the pendulum, followed by the application of a predictive controller to this model. The proposed algorithm is subsequently validated on an experimental platform, the Quanser QUBE-Servo2. The paper emphasizes the advantages of MPC, particularly its ability to incorporate system constraints and effectively manage nonlinear dynamics, thus making it a widely adopted strategy in industrial applications. Nevertheless, it also addresses the inherent challenges of MPC implementation, notably the construction of accurate system models and the computational burden associated with solving complex optimization problems. The control objective is to maintain the pendulum in its upright equilibrium position. The study evaluates the effectiveness of MPC with and without uncertainty compensation by analyzing key performance metrics, including response time, settling time, overshoot, and steady-state error, through both simulations and experiments. The results illustrate the comparative benefits and limitations of the uncertainty-compensated MPC algorithm relative to the traditional MPC controller.