This paper is devoted to design of a non-linear model predictive controller (NMPC), which will swing-up and stabilize an inverse rotary pendulum known as the Furuta Pendulum. This paper presents a simulation validation of the NMPC strategy using a full-fidelity non-linear mathematical model of the Furuta pendulum obtained from the Euler-Lagrange motion equations. The NMPC strategy was implemented in MATLAB using the MATMPC toolbox.

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Rapid growth of the human population has led to various problems, such as massive overload of wastewater treatment plants. Therefore, optimal control of these plants is a relevant subject. This contribution analyses control of a cascade of ten biochemical reactors using simulation results with the aim to design optimal and predictive control strategies and to compare the achieved control performance. The plant represents a complicated process with many variables involved in the model structure, reduced to the single-input and single-output system. The first implemented approach is linear offset-free model predictive control which provides the optimal input trajectory minimising a quadratic cost function. The second control strategy is robust model predictive control with similar features as model predictive control but including the uncertainty of the process. The final approach is generalised predictive control, mostly used in the industry because of its simple structure and sufficiently good control performance. All considered predictive controllers provide satisfactory control performance and remove the steady-state control error despite the constrained control inputs.