In this study, we propose a real-time nonlinear predictive control of a thermal process. The nonlinear dynamics of the process are mathematically modeled using a fractional Hammerstein structure. This estimated model is then employed as a prediction model in a nonlinear model predictive control scheme over a finite prediction horizon. The proposed algorithm generates the control sequence by optimizing a cost function with iterative nonlinear optimization methods, including the Nelder-Mead and gradient-based approaches. To evaluate its effectiveness, the nonlinear predictive control algorithm is implemented on an electric oven using the STM32F407VG micro-controller and compared in the tracking performances to a conventional proportional integral controller.
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