Intelligent nonlinear optimal controller of a biotechnological process
Wybrane pełne teksty z tego czasopisma
Designing an effective criterion and learning algorithm for finding the best structure is a major problem in the control design process. In this paper, the fuzzy Proportional Parallel Distributed Compensation with Reduced Rule Base approach (PPDC_RRB) is proposed. The design problem considered is essentially nonlinear optimal and robust control problem due to the nonlinear nature of the Takagi-Sugeno fuzzy system. The control signal thus obtained will minimize performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the PPDC_RRB controller. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The chaotic mutation is introduced for maintaining the population diversity during the evolution process of the genetic algorithm. The performances of the PPDC_RRB are compared with those found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed PPDC_RRB and PD_GO has successfully met the design specifications.
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