Rainer Palm

Sliding mode fuzzy control

Fuzzy controllers work like modified sliding mode controllers (SMCs). Compared to ordinary SMCs, fuzzy controllers (FCs) have the advantage of higher robustness. The structure of a FC is derived from a nonlinear state equation representing a large class of physical systems. The following aspects are discussed: stability conditions, scaling of the state vector, choice of the switching line, and determination of the break frequencies of the controller. By the choice of an additional boundary layer in the phase plane the FC is modified so that drastic changes of the manipulated variable can be avoided, especially at the boundary of the normalized phase plane. In this context, the higher robustness of the modified FC over the modified SMC is discussed. An FC for a higher-order system is proposed.<<ETX>>

Robust control by fuzzy sliding mode

Most fuzzy controllers (FCs) for nonlinear second order systems are designed with a two-dimensional phase plane in mind. We show that the performance and the robustness of this kind of FC stems from their property of driving the system into the sliding mode (SM), in which the controlled system is invariant to parameter fluctuations and disturbances. Additionally, the continuous distribution of the control values in the phase plane causes a behavior similar to that of a sliding mode controller (SMC) with a boundary layer (BL) near the switching line. This gives assured tracking quality even in the presence of high model uncertainties. Tracing the FC back to the principle of an SMC one obtains evidence about the stability of the closed-loop system. The choice of the scaling factors for the crisp inputs and outputs can be guided by the comparison of the FC with the SMC and with the modified SMC, respectively. At the end of the paper, an FC for a higher-order system is proposed. Simulation results show the practicability of the method.

Observers for Takagi-Sugeno fuzzy systems

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.

An adaptive fuzzy sliding-mode controller

This paper deals with a new adaptive fuzzy sliding-mode controller and its application to a robot manipulator arm. The theory for this approach and for the heuristics-based linguistic adaptation is presented, and a mathematical description is derived. Furthermore, an application of this adaptive controller for a two-link robot arm is shown. The obtained results show the high efficiency of the new controller type.

Fuzzy-Systems in Computer Science

Fuzzy control neural fuzzy systems fuzzy systems and artificial intelligence fuzzy classification theoretical aspects of fuzzy systems.

Fuzzy switched hybrid systems-modeling and identification

The combination of hybrid systems and fuzzy multiple model systems is described. Further, a hierarchical identification of the resulting fuzzy switched hybrid system is outlined. The behavior of the discrete component is identified by black box fuzzy clustering and subsequent parameter identification taking into account some prior-knowledge about the discrete states. The identification of the continuous models for each discrete state is done based on local linear fuzzy models.

Advances in Fuzzy Control

Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.

Sliding mode observer for a Takagi Sugeno fuzzy system

Deals with a sliding mode fuzzy observer for nonlinear plants. A nonlinear plant is approximated by a multiple model Takagi Sugeno (TS) fuzzy system and then transformed into a canonical form for which a nonlinear observer is constructed. The paper presents a type of sliding mode fuzzy observer that deals with matched and unmatched uncertainties in the plant model very effectively.

Fuzzy controller for a sensor guided robot manipulator

Sensor guided robots require, according to nonlinear characteristics of certain interaction processes between robot effector and object, appropriate robust control algorithms for the external sensory control loop. This paper deals with a fuzzy controller which bases on linguistic production rules. Following the pattern of the suboptimal control method its structure is available in a large field of applications. Simulations and real time experiments concerning the contour tracking problem show, compared with a conventional PD-controller, the high tracking quality of the controller.

Soft computing optimization methods applied to logistic processes

This paper discusses the methodologies that can be used to optimize a logistic process of a supply chain described as a scheduling problem. First, a model of the system based on a real-world example is presented. Then, a new objective function called Global Expected Lateness is proposed, in order to describe multiple optimization criteria. Finally, three different optimization methodologies are proposed: a classical dispatching rule, and two soft computing techniques, Genetic Algorithms (GA) and Ant Colony Optimization (ACO). These methodologies are compared to the dispatching policy in the real-world example. The results show that dispatching heuristics are outperformed by the GA and ACO meta-heuristics. Further, it is shown that GA and ACO provide statistically identical scheduling solutions and from the optimization performance point of view, it is equivalent to use any of the meta-heuristics.