Synthesis of the neuro-fuzzy regulator with genetic algorithm

Abstract

Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

“…Metaheuristic algorithms have provided greater stabilization and better error performances when the fuzzy set method is implemented because problems have grown in complexity. For example, in [18], an adaptive neuro-fuzzy inference system and genetic algorithm (NFGA)-based MPPT controller for the PV system is presented and in [19], a synthesis of the neuro-fuzzy regulator with genetic algorithm is analyzed. Additionally, in [20], a new fuzzy approach to dynamic adaptation applied to the marine predator algorithm is presented, and in [21], an improved chimp optimization algorithm is presented.…”

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“…This is especially characteristic of objects with high energy intensity. At present, such scientists as Siddikov et al [9]- [12], actively research to improve control systems of technological processes based on energy-saving technologies with the use of modern control methods. One of the ways to solve this problem is the creation of adaptive-intelligent control systems of technological processes that have the properties of automation of determining the optimal tuning parameters of the PID controller, both in the design process and in operation.…”