Analysis Of Fuzzy Control Systems Research Articles

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

Power consumption control of multi-pump systems of the main water drainage in underground mines based on the Mamdani fuzzy inference system

The article considers synthesis of an expert system for controlling electric power consumption by pumps of main water drainage facilities of an underground mine on the basis of the Mamdani fuzzy inference algorithm. The proposed system has a MISO-structure (multiple-input, single-output) with two input variables, such as water inflow and power cost as well as one output coordinate – power of pumping units. Two bases of fuzzy rules such as conjunction (AND) and disjunction (OR) are formed. By simulation modelling, a comparative analysis of fuzzy control systems for power consumption by water drainage facilities is carried out, as well as a system without control, when the pump performance is stabilized, during week and month periods. It is established that OR-rule based systems can reduce power costs by 1.89% during a week and by 2.28% during a month, and AND-rule based systems by 4.13%, as well as by 5.43% during week and month, respectively. At the same time, we note that the economic effect is achieved not through a decrease in power consumption, but by adjusting the operation mode of the water drainage facility, which involves ensuring maximum efficiency of groundwater drainage when the power cost is high, and minimum efficiency when it is low.

Study on Clinical Application of Intelligent Control Technology

The main research direction of intelligent control technology is analyzed and discussed in detail, including fuzzy model and basic control algorithm, fuzzy predictive control, stability analysis of fuzzy control system. The status and function of intelligent control are discussed, and the application and development of intelligent control in medicine are summarized and prospected in this paper.

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

SUMMARY This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur’e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

Sensitivity Analysis of a Class of Fuzzy Controlled Mobile Robots

The paper deals with the sensitivity analysis of fuzzy control systems solving the tracking control problem for a class of wheeled mobile robots with two degrees of freedom. There is proposed a development method for PI-fuzzy controllers based on the application of the Extended Symmetrical Optimum method to the basic linear PI controllers. A cascaded control system structure is considered employing the off-line generation of the reference inputs by means of the artificial potential field method for obstacle avoidance. Digital simulation results for a case study validate the theoretical approaches.

Stability analysis and systematic design of Takagi–Sugeno fuzzy control systems

The stability analysis and systematic design techniques of Takagi–Sugeno (T–S) fuzzy control systems are discussed in this paper. In order to make good use of the structural information in rule base to decrease the conservatism of the stability analysis, the standard fuzzy partition of the input variables of fuzzy control systems is well defined. Based on the property analysis of fuzzy control systems with SFP inputs, the stability of both the input-free T–S fuzzy systems and the closed-loop T–S fuzzy control systems are studied in detail with the extended Lyapunov theory, and two theorems to check the stability of T–S fuzzy control systems are proposed. These methods, which only require to find a common positive-definite symmetric matrix in each maximal overlapped-rules group, can reduce the conservatism and difficulty of the former stability analysis approaches. Using the method of parallel distributed compensation, a systematic design approach of T–S fuzzy control systems is presented. The simulation results of three examples show that these methods are effective.

Risk analysis of fuzzy control systems with ( n+1)-inputs and 1-output FLC

This paper proposes a fundamental method to analyze the uncertain fuzzy control systems by the methodology of functional analysis based on the fuzzy mapping concept. First of all, a completely continuous operator is defined, and some fuzzy mapping simultaneous equations are introduced by using the defined operator. In the next place, the existence conditions of the likely solutions of the fuzzy mapping simultaneous equations are discussed for the given membership grades. Last of all, it is shown that the simultaneous equations correspond to the input–output relation of the uncertain control systems with the ( n+1)-inputs and 1-output fuzzy logic controllers, and this mathematical discussion is applied to the risk analysis of the systems.

A new approach to numerical stability analysis of fuzzy control systems

The paper presents numerical methodology for the stability analysis of a fuzzy control system. The fuzzy control system analyzed is a closed-loop system controlled by a fuzzy logic controller (FLC) with singleton consequents. Compared with previous works based on numerical approaches (E. Kim et al., 1999), the method proposed in the paper employs two new strategies to release the conservatism of the previous methods: region-wise affine transformation and piecewise quadratic Lyapunov function. Finally, the effectiveness of the stability analysis is illustrated by a numerical example and its computer simulation.

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Stability analysis of fuzzy control systems

In this paper, we present a general review of the stability issue as related to fuzzy control systems. The concept of stability and the general criterion used for fuzzy control systems are discussed. The intuitive and nonlinear system stability analysis of fuzzy control systems are given. Popov's technique is proposed to test the stability of fuzzy systems.

Numerical stability analysis of fuzzy control systems via quadratic programming and linear matrix inequalities

This paper proposes a numerical stability analysis methodology for the singleton-type linguistic fuzzy control systems based on optimization techniques. First, it demonstrates that a singleton-type linguistic fuzzy logic controller (FLC) can be converted into a region-wise sector-bounded controller or, more generally, a polytopic system by quadratic programming (QP). Next, the convex optimization technique called linear matrix inequalities (LMI) is used to analyze the closed loop of the converted polytopic system. Finally, the applicability of the suggested methodology is highlighted via simulation results.

Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems

Points out how the nonlinearities involved in multivariable Takagi-Sugeno (T-S) fuzzy control systems could originate complex behavior phenomena, such as multiple equilibrium points or limit cycles, that cannot be detected using conventional stability analysis techniques. In the paper, the application of MIMO frequency-domain methods to predict the existence of multiple equilibria and of limit cycles are presented. The proposed method is based on the formulation of a Lur'e problem from the original structure of a T-S fuzzy system with a fuzzy controller. Furthermore, this technique makes straightforward the application of input-output stability techniques such as the multivariable circle criterion, also called the conicity criterion, and the harmonic balance method. Moreover, in the paper, the application of the harmonic balance method has been generalized to the case of a MIMO fuzzy system with asymmetric nonlinearities and improved by the decreasing conservatism. A new and more general stability index which could be used to perform a bifurcation analysis of fuzzy control systems is presented. The paper includes a collection of examples where the advantages of the proposed approach are made explicit comparing it to the input-output conicity criterion and the Lyapunov direct method.

Robust stability analysis of fuzzy control systems

Robust stability of fuzzy control systems is studied through the Popov-Lyapunov approach. First, we transform a fuzzy control system into a Lur'e system with uncertainties or nonlinearities. Second, the Lyapunov's direct method is used to guarantee the stability of the perturbed Lur'e system, and then a robustness measurement which gives the bound on allowable uncertainties or nonlinearities is derived. Also, if the bound of the uncertainties/nonlinearities of the system is known, we can find an estimated region of asymptotic stability in phase space. A nonlinear numerical example and a magnetic bearing experiment demonstrating the feasibility of the method are illustrated.

ペトリネットによるファジィ制御系の安定解析

ファジィ制御は、一般にif-then型のファジィルールにより制御規則の記述を行ない、ファジィ推論により操作量を求める手法であり、熟練者の経験に基づいたあいまいな制御を行なうことができるため、さまざまな分野で応用されている。ファジィ制御器の問題点として安定性の保証が困難であることが指摘されているが、近年ではファジィ制御器の安定性に関する研究が進められている。しかし、従来の安定解析手法はファジィルールを数式表現に置き換えることでなされており、ファジィ制御の分かりやすいという特徴を損なってしまっている。本論文では、制御対象および制御器のファジィルールを離散事象表現とみなすことで、ファジィ制御系をペトリネットを用いて表現し、状態推移関数を用いた安定性の解析手法を提案する。制御系のとりうる状態はペトリネットのプレースで表され、現在の制御系の状態はプレースのトークンの有無によって表される。また、ファジィルールの発火はトランジションの発火として表現される。本手法はファジィ制御の持つ挙動の分かりやすさという特徴を損なうことなく、ファジィ制御系の解析を可能にしている。本論文では、簡単な制御系に対して本手法を適用しその有用性を示す。

Stability analysis of fuzzy control systems

A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy state feedback controller is proposed. Stability of the fuzzy control system is discussed and a sufficient condition to guarantee the stability of the system is given in terms of uncertain linear system theory. The results in this paper improve our previous stability results. An example is used to show the proposed method.

Stability analysis of fuzzy control systems using facet functions

Stability analysis of fuzzy control systems using facet functions

Stability Analysis of Fuzzy Control System by 3D Space Display

Recently fuzzy control has been applied to various systems such as home electronics, manufacturing processes and production plants. Because applications to actual control systems can be risky, the stability of a fuzzy control system must be insured. For stability analysis of fuzzy control, phase plane analysis, Hurwitz's analysis in subareas, and Lyapunov's method have been proposed. In this paper, we propose a new method of stability analysis, that is, stability discrimination on the basis of display of the stability area in 3D space, the manipulated variable, the control error, and the deviation of its error. The stability is discriminated according to whether the fuzzy control map exists in the partial space where Hurwitz's stable condition is satisfied or not.

Analysis and design of fuzzy control system

A continuous-time fuzzy control system which is composed of a fuzzy model and a fuzzy controller is proposed. Under certain conditions, the proposed fuzzy control system can be regarded as a Fuzzy-PID control system. The stability analysis of the fuzzy control system is given. A sufficient condition to guarantee the stability of the proposed fuzzy control system is proposed in terms of Lyapunov's method. The results can be applied to the design of a Fuzzy-PID control system. The design procedure can also be applied to the controller design with a non-fuzzy model. Examples are given to illustrate the design of the proposed fuzzy control system.

Stability Analysis of Fuzzy Control Systems Based on Phase Plane Analysis

Stability Analysis of Fuzzy Control Systems Based on Phase Plane Analysis

Special Issue "Fuzzy/Neural Network Applications to Dynamics and Control of Mechanical Systems". Stability Analysis of Fuzzy Control System by 3D Space Display.

Recently fuzzy control has been applied to various systems such as home electronics, the process, and plants. Because applications to actual control systems can be risky, the stability of fuzzy costrol system must be insured. As stability analysis of fuzzy control, phase plane analysis, Hurwitz's analysis in subareas, and Lyapunov's method have been proposed. In this paper, we propose a new method of stability analysis, that is, stability discrimination on the basis of display of the stability area in 3D space, the manipulated variable, the control error, and the deviation of its error.