A Study of Adaptive Model Predictive Control for Rotary Inverted Pendulum - Huynh, Phuc-Hoang; Le, Khac-Chan-Nguyen; Nguyen, Truong-Phuc; Tran, Hoang-Dang-Khoa; Dang, Su-Truong; Nguyen, Thanh-Quyen; Le, Thang-Phong; Nguyen, Huu-Hanh; Tran, Pham-Hong-Linh; Nguyen, Hau-Phuong; Nguyen, Hoang-Son; Nguyen, Tai-Truong; Nguyen, Hai-Thanh (Journal of Fuzzy Systems and Control, 2025)
This paper proposes an Adaptive Model Predictive Control (MPC) approach for the rotary inverted pendulum (RIP). The method combines Linear Time-Varying (LTV) models at each sampling instant with a Linear Time-Varying Kalman Filter (LTVKF) for state estimation. By predicting and adapting to dynamic system changes, the controller achieves trajectory tracking performance comparable to non-adaptive MPC. However, the Adaptive MPC extends the arm’s operating range by up to 1.5 times, making it a promising solution for strongly nonlinear or time-varying systems like the RIP.