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Intelligent nonlinear optimal controller of a biotechnological process

Soukkou A., Leulmi S., Khellaf H.

Archives of Control Sciences

2009

Vol. 19, no. 2

217-240

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.

Multiobjective optimization of a fuzzy PID controller

Soukkou A., Khellaf A. H., Leulmi S.

Archives of Control Sciences

2006

Vol. 16, no. 4

445-461

A fuzzy logic controller with multilayer neutral network whose synaptic weights represent the fuzzy knowledge base and its application to the highly nonlinear systems is presented in this work. The scaling factors of the input variables, membership functions and the rule sets are optimized by the use of the multiobjective genetic algorithms. The fuzzy network structure is specified by a combination of the mixed Takagi-Sugeno's and Mamdani's fuzzy reasoning. The mixed, Binary-Real-Integer, optimal coding is utilized to construct the chromosomes, which define the same of necessary prevai;ling parameters for the conception of the desired controller. This new controller stands out by a non-standard gain, which varies lineary with the fazzy inputs. Under certain conditions, it becomes similar to the conventional PID controller with non-linearly variable coefficients. Computer simulation indicates that the designed fuzzy controller is satisfactory in control of a nonlinear system "Inverted Pendulum".