Fuzzy Model-based Control Systems Research Articles

Quadratic-Stability Analysis of Fuzzy-Model-Based Control Systems Using Staircase Membership Functions

This paper presents the stability analysis of fuzzy-model-based (FMB) control systems. Staircase membership functions are introduced to facilitate the stability analysis. Through the staircase membership functions approximating those of the fuzzy model and fuzzy controller, the information of the membership functions can be brought into the stability analysis. Based on the Lyapunov-stability theory, stability conditions in terms of linear-matrix inequalities (LMIs) are derived in a simple and easy-to-understand manner to guarantee the system stability. The proposed stability-analysis approach offers a nice property that includes the membership functions of both fuzzy model and fuzzy controller in the LMI-based stability conditions for a dedicated FMB control system. Furthermore, the proposed stability-analysis approach can be applied to the FMB control systems of which the membership functions of both fuzzy model and fuzzy controller are not necessarily the same. Greater design flexibility is allowed to choose the membership functions during the design of fuzzy controllers. By employing membership functions with simple structure, it is possible to lower the structural complexity and the implementation cost. Simulation examples are given to illustrate the merits of the proposed approach.

Design of an embedded fuzzy PD controller for thermal comfort applications

In this paper, a reasoning-based intelligent system makes use of fuzzy control approach, which is designed for thermal comfort applications by using an embedded microcontroller system. In general, it is not possible to implement the mathematical thermal comfort models in actual environment. Thermal comfort usually depends on four environmental parameters and two personal parameters. Normally, only air temperature and humidity could be controlled in conventional heating ventilation and air conditioning (HVAC) systems. Managing and controlling the temperature and humidity parameters are very important for creating healthy living and comfortable working places. As known, fuzzy logic allows complex control system design directly from human experience. Therefore, preferring fuzzy model-based control systems to traditional ones may provide valuable advantage in applications requiring different control strategies for personal choices like thermal comfort control in buildings. So, in this paper, a comfortable fuzzy control system is investigated and performance of this suggested system is tested by Proteus virtual system modelling software. As a result, success of system is founded acceptable from view of the thermal comfort.

Robust Stability Analysis of TS-Fuzzy-Model-Based Control Systems with Both Elemental Parametric Uncertainties and Norm-Bounded Approximation Error

This paper discusses the robust stability of Takagi-Sugeno (TS) fuzzy model based control systems. First, it is pointed out that the complex nonlinear control systems with uncertain parameters can be represented as the TS-fuzzy-model-based control systems with both elemental parametric uncertainties and norm-bounded approximation error. Next, a sufficient condition is proposed in terms of linear matrix inequalities (LMIs) for ensuring that the TS-fuzzy-model-based control systems with both elemental parametric uncertainties and norm-bounded approximation error are robustly stable. Some examples are given to show that the proposed sufficient LMI condition may obtain less conservative results than the existing ones reported recently, owing to the proposed sufficient condition taking the elemental information of the parametric uncertain matrices into consideration.

Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm

This paper presents stability analysis of fuzzy model-based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using a genetic algorithm (GA) with arithmetic crossover and nonuniform mutation. A stability condition is derived based on Lyapunov's stability theory with a smaller number of Lyapunov conditions. A solution of the stability conditions is also determined using GA. An application example of stabilizing a cart-pole typed inverted pendulum system is given to show the stabilizability of the nonlinear controller.

The Implementation of a Relational Fuzzy Model Based Control System on an Industrial Drying Process

AbstractA fuzzy logic control system for difficult non-linear processes has been developed. This controller is structured in a very similar manner to the standard Internal Model Control design, differing only from the conventional system in that it uses a fuzzy model. The relational fuzzy model used is different from the more common Rule based or expert system, in that it uses no formal rules. A relational model of the process to be controlled is constructed by firstly assuming that all the process inputs and outputs are related. Then by using gathered process input/output data the strengths of these relationships are found and the relational fuzzy model is constructed. The relational fuzzy model-based control system has previously been tested extensively on simulations and lab-scale experiments. The control problem investigated is the control of moisture content in the outlet product from a dryer. The dryer is used to process material left over from a fermentation/brewing process. The spent grain from th...

Modelling and robust control of uncertain dynamical systems using fuzzy set theory

New modelling and control approaches for uncertain nonlinear dynamical systems using fuzzy set theory are developed and proposed in this paper. The philosophy of the proposed modelling approach is to provide a new fuzzy-set based representation of uncertain systems so that robust control design is made feasible without resorting to model simplification or imposing restrictive conditions on system uncertainty (heretofore required by most traditional control design approaches). Based on the new generated fuzzy model representation of uncertain systems, a fuzzy control design approach is also proposed. The latter consists of extending design techniques of the sliding mode control method to fuzzy systems. Accordingly, new control design concepts using fuzzy sets are introduced. The stability analysis of the proposed fuzzy-model based control system is also provided in this paper. The benefits of the present study are illustrated through numerical simulations of the fuzzy modelling and control of a single-link robot arm with uncertain dynamics.