Abstract
In this paper, an approach for the tuning of a model-based non-linear predictive control (NMPC) is presented. The proposed control uses the pattern search optimization algorithm (PSM), which is applied to the pH non-linear control in the alkalinization process of sugar juice. First, the model identification is made using the Takagi Sugeno T-S fuzzy inference systems with multidimensional fuzzy sets; the next step is the controller parameters tuning. The PSM algorithm is used in both cases. The proposed approach allows the minimization of model uncertainty and decreases, in the response, the error in a steady state when compared with other authors who perform the same procedure but apply other optimization algorithms. The results show an improvement in the steady-state error in the plant response.
Highlights
The existence of productive processes with strong non-linearity forces different control strategies, One of them is adaptive control
It is identified that the models obtained using fuzzy inference systems SIB T-S and CBMD present a favorability for control-oriented applications, demonstrated to be more flexible in chemical processes with application in the industry
Improving their efficiency because they avoid the contribution of the uncertainties inherent to the model; The emergence of a novel optimization method such as the pattern search optimization algorithm (PSM) pattern search method is established, which presents essential advantages that allow the construction of a clear methodology for tuning an NMPC with SIB T-S in combination with a CBMD
Summary
The existence of productive processes with strong non-linearity forces different control strategies, One of them is adaptive control. Optimization strategies, such as genetic algorithm (GA), evolution strategic (ES), and particle swarm optimization (PSO) are made up of a population of particles, which are the starting point of an optimal value, which in some cases shows stagnation in local minimums [6,7,8,9] In this range of optimization methods for the tuning of fuzzy control systems with PID structures, classical techniques such as gradient algorithms and Rosenbrok’s algorithms are used [10]. The conditions generated by the surveys give the guidelines for reducing the current mesh, ensuring algorithm convergence In such a way that it presents a satisfactory operation for applications with multiple local minimum [31] and is flawlessly applied to NMPC techniques such as those presented in this article.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
