A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discrete-time nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried out, and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty, which may include modeling error and disturbances. In addition, bounds on the asymptotic and transient performance are given. The main assumptions on the system and model are that they must not have strong nonminimum-phase effects, except time-delay, and the unstructured uncertainty must not be too large. A simulation example illustrates some of the properties of the modeling method and model based control structure. >

Fuzzy model based control: stability, robustness, and performance issues

Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual "fuzzy PD" controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a usual fuzzy PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples. >

A two-layered fuzzy logic controller for systems with deadzones

In this study, a design methodology is introduced that blends the classical PID and the fuzzy controllers in an intelligent way and thus a new intelligent hybrid controller has been achieved. Basically, in this design methodology, the classical PID and fuzzy controller have been combined by a blending mechanism that depends on a certain function of actuating error. Moreover, an intelligent switching scheme is induced on the blending mechanism that makes a decision upon the priority of the two controller parts; namely, the classical PID and the fuzzy constituents. The simulations done on various processes using the new hybrid fuzzy PID controller provides ‘better’ system responses in terms of transient and steady-state performances when compared to the pure classical PID or the pure fuzzy controller applications. The controller parameters are all tuned by the aid of genetic search algorithm.

An Intelligent Hybrid Fuzzy Pid Controller

A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

P. Tam

Papers

Linguistic PID Controllers