Sinn-Cheng Lin

Design of self-learning fuzzy sliding mode controllers based on genetic algorithms

Abstract In this paper, genetic algorithms were applied to search a sub-optimal fuzzy rule-base for a fuzzy sliding mode controller. Two types of fuzzy sliding mode controllers based on genetic algorithms were proposed. The fitness functions were defined so that the controllers which can drive and keep the state on the user-defined sliding surface would be assigned a higher fitness value. The sliding surface plays a very important role in the design of a fuzzy sliding mode controller. It can dominate the dynamic behaviors of the control system as well as reduce the size of the fuzzy rule-base. In conventional fuzzy logic control, an increase in either input variables or the associated linguistic labels would lead to the exponential growth of the number of rules. The number of parameters or the equivalent length of strings used in the computations of genetic algorithms for a fuzzy logic controller are usually quite extensive. As a result, the considerable computation load prevents the use of genetic operations in the tuning of membership functions in a fuzzy rule-base. This paper shows that the number of rules in a fuzzy sliding mode controller is a linear function of the number of input variables. The computation load of the inference engine in a fuzzy sliding mode controller is thus smaller than that in a fuzzy logic controller. Moreover, the string length of parameters is shorter in a fuzzy sliding mode controller than in a fuzzy logic controller when the parameters are searched by genetic algorithms. The simulation results showed the efficiency of the proposed approach and demonstrated the applicability of the genetic algorithm in the fuzzy sliding mode controller design.

Design of adaptive fuzzy sliding mode for nonlinear system control

An adaptive fuzzy sliding mode controller (AFSMC) is proposed. The parameters of the membership functions in the fuzzy rule base are changed according to some adaptive algorithm for the purpose of controlling the system state to hit a user-defined sliding surface and then slide along it. The initial IF-THEN rules in the AFSMC can be randomly selected or roughly given by human experts, and then automatically tuned by a direct adaptive law. Therefore, the reduction of the expertise dependency in the design procedure of fuzzy logic control is called the rule tolerance property. By applying the AFSMC to control a nonlinear unstable inverted pendulum system, the simulation results showed the expected approximation sliding property, and the dynamic behavior of control system can be determined by the sliding surface.<<ETX>>

A GA-based fuzzy controller with sliding mode

In this study, the genetic algorithms are applied to find out a nearly optimal fuzzy rule-base for fuzzy sliding mode controller in the sense of fitness. In conventional fuzzy logic controllers (FLC), linearly increasing in either input variables or input linguistic labels would lead the number of rules grow up exponentially. Since the larger size of rule base would cause the longer string length and higher computing load, it becomes one of the difficulties of realizing genetic algorithms to search the suitable rules or membership functions for fuzzy logic controllers. This paper will show that the number of rules in fuzzy sliding mode controller (FSMC) is a linear function of input variables, such that the inferring load of the inference engine in FSMC is more light than that of FLC, and the string length of unknown parameters in FSMC is shorter than that in FLC. Therefore, using genetic algorithms to search fuzzy rules or membership functions for FSMC becomes more economical and applicable. The simulation results verify the efficiency of proposed approach.<<ETX>>

RBF-network-based sliding mode control

A sliding mode controller (SMC) design method based on radial basis function network (RBFN) is proposed in this paper. Similar to the multilayer perceptron, the RBFN also known to be a good universal approximator. In this work, the weights of the RBFN are changed according to some adaptive algorithms for the purpose of controlling the system state to hit a user-defined sliding surface and then slide along it. The initial weights of the RBFN can be set to small random numbers, and then online tuned automatically, no supervised learning procedures are needed. By applying the RBFN-based sliding mode controller to control a nonlinear unstable inverted pendulum system, the simulation results show the expected approximation sliding property was occurred, and the dynamic behavior of the control system can be determined by the sliding surface.<<ETX>>

Real-coded genetic algorithm based fuzzy sliding-mode control design for precision positioning

A fuzzy sliding-mode controller was optimized through real-coded genetic algorithms and successfully implemented on an industrial XY table. The fuzzy sliding-mode controller is special type of fuzzy controller. By using the sliding surface, the fuzzy rule is simpler and the entire rule base is more compact. Thus, more easy to apply self-learning schemes. The real-coded genetic algorithm uses the internal floating-point representation of the computer system. With this advantage, the finite resolution problem of traditional genetic algorithm has been solved. The experimental results show the success of this approach.

Vision based fruit sorting system using measures of fuzziness and degree of matching

Fuzzy approaches were used to determine optimal thresholding values of fruits images, and fuzzy degree of matching was applied to classify the color and size of fruit. Results showed that fuzzy method was superior to the traditional statistical methods, and a accuracy of 93.3% for combined sorting was reported. The errors due to miscategorization could thus be reduced if the fuzzy methods were used. The developed fuzzy algorithms were integrated with the machine vision guided robotic sorting system for fruits.<<ETX>>

Apply fuzzy seeking control to high precision hard disk driver

This paper describes the application of fuzzy logic control on the track-seeking motion in the head-positioning servomechanism of a hard disk. There are two major operations in the head-positioning servo of a hard disk, i.e. seeking and tracking. The seeking controller performs minimum-time movement of the read-write heads from their current track position to a target track specified by the file controller. The dynamic behaviors of the head-positioning servo is highly nonlinear, which make the derivations of an analytic model very difficult. A fuzzy seeking controller is designed and implemented on a high precision hard disk driver, the Zentek ZM3140. The proposed seeking controller can successfully move the read-write head servo mechanism to the desired target track. Our experimental results also show fast responses and robust behaviors of the servo system.

Nonlinear input mapping in fuzzy control systems

The input scaling factors in a fuzzy control system are often used as a transformation from the real input data to the desired space. Sometimes, they are tuned for better performance just as the coefficients in a PID controller. Theoretically, they are constant and should be adjusted for better performance while the operating point is changed. In this paper, the authors suggest nonlinear mapping functions to substitute the role of input scaling factors. The results show how the nonlinear mapping function work and the performance could be better by proper adjustment. It is also noticed that such mapping will change the shapes of the membership functions. That means one need not tune every membership function of linguistic variables, and just choosing a proper nonlinear mapping function could achieve the same effects. The authors use an inverted pendulum system to verify the results.