Research on Nonlinear Systems Modeling Methods Based on Neural Networks

Nonlinear systems widely exist in all fields of industrial production and are difficult to model because of complex non-linearity. Neural network is widely used in process prediction, fault detection and fault diagnosis of modern industry because of the nonlinear fitting ability. Due to various structures, there exists diversity in the performance of neural networks. However, only the appropriate network can improve the efficiency and safety in modelling nonlinear industrial process, which requires full consideration of the structure of neural network. In this study, several typical structures of neural networks are compared and analysed, and the performance differences caused by these structures are presented in detail. Finally, performance differences of neural networks with inconsistent structures are verified on several experiments. The results showed that neural networks with inconsistent structures were good at dealing with different types of nonlinear systems. Our work will provide a theoretical basis in accurately modeling the industrial production process, which is beneficial to nonlinear system control.

Investigations on Nonlinear Tito System using Passivity Based Control in Real Time

The concept of passivity for nonlinear systems attracted new interest in nonlinear system control. This work aims to develop a control algorithm using Passivity Based Control (PBC) for a Two Input Two Output (TITO) system namely spherical-conical interacting coupled tank system in real time. Conical tank spherical tank combination together with the interaction makes the system highly nonlinear system. Real time model of the SISO and TITO systems are obtained experimentally by black box modeling. A computer based control system is developed and the process data is obtained using USB based VUDAS-100 data acquisition module. The algorithm for control is developed in the PC with conventional PI controller and also with PBC and the controller performance is compared. The robustness of the controllers is validated by imposing both servo and regulatory operations. Both the SISO and TITO system results obtained in real time shows that PBC works efficiently with improved performance compared to ZN based PI controller

Analytical Synthesis of Regulators for Nonlinear Systems with a Terminal State Method on Examples of Motion Control of a Wheeled Robot and a Vessel

The paper is devoted to several examples of control algorithm development for two-wheeled double-track robot and low-tonnage vessel-catamaran with two Azipods that show practical aspects of the application of one nonlinear system control method — terminal state method. This method, developed by the authors of the present paper, belongs to the class of methods for inverse dynamics problem solving. Mathematical models of control objects in the form of normal systems of third-order nonlinear differential equations for the wheeled robot and seventh-order ones for the vessel are presented. Design formulas of the method in general form for terminal and stabilizing controls are shown. A routine of obtaining calculation expressions for control actions is shown. Results of computer simulation of bringing the robot to a given point in a given time, as well as bringing the vessel to a given course during a “strong” maneuver, are described.

Nonlinear system control using a fuzzy cerebellar model articulation controller involving reinforcement-strategy-based bacterial foraging optimization

This article proposes a fuzzy cerebellar model articulation controller with reinforcement-strategy-based modified bacterial foraging optimization for solving the cart-pole balancing control problem. The proposed reinforcement-strategy-based modified bacterial foraging optimization is used to adjust the parameters of fuzzy receptive field functions and fuzzy weights for improving the accuracy of the fuzzy cerebellar model articulation controller output. An efficient strategic approach is applied in the chemotaxis step in the traditional bacterial foraging optimization algorithm. In the approach, each virtual bacterium swims for different run lengths and increases the bacterial diversity. Experimental results are presented to show the performance and effectiveness of the proposed reinforcement-strategy-based modified bacterial foraging optimization method.