This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Probability and Measure

Binary grey wolf optimization approaches for feature selection

In classification, feature selection is an important data pre-processing technique, but it is a difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes three new initialisation strategies and three new personal best and global best updating mechanisms in PSO to develop novel feature selection approaches with the goals of maximising the classification performance, minimising the number of features and reducing the computational time. The proposed initialisation strategies and updating mechanisms are compared with the traditional initialisation and the traditional updating mechanism. Meanwhile, the most promising initialisation strategy and updating mechanism are combined to form a new approach (PSO(4-2)) to address feature selection problems and it is compared with two traditional feature selection methods and two PSO based methods. Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. PSO(4-2) outperforms the two traditional methods and two PSO based algorithm in terms of the computational time, the number of features and the classification performance. The superior performance of this algorithm is due mainly to both the proposed initialisation strategy, which aims to take the advantages of both the forward selection and backward selection to decrease the number of features and the computational time, and the new updating mechanism, which can overcome the limitations of traditional updating mechanisms by taking the number of features into account, which reduces the number of features and the computational time.

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Particle swarm optimisation for feature selection in classification

A survey on new generation metaheuristic algorithms

Differing from previous studies, where sliding mode control theory-based rules are proposed for only the consequent part of the network, the developed algorithm in this paper applies fully sliding mode parameter update rules for both the premise and consequent parts of the interval type-2 fuzzy neural networks. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Then, the performance of the proposed learning algorithm is tested on the identification of wing flutter data set available online as a benchmark system and the prediction of Mackey-Glass chaotic system. The simulation results indicate that the proposed algorithm is significantly faster than the gradient-based methods as well as providing a slightly better identification performance. The reason for the fast convergence is that the proposed parameter update rules do not have any matrix manipulations which makes them simple to be implemented in real-time systems. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Another prominent feature of the proposed learning algorithm is to have a closed form which makes it easier to implement than the other existing learning methods, e.g. gradient-based methods.

Stabilization of type-2 fuzzy Takagi-Sugeno-Kang identifier using Lyapunov functions

Even if we have highly capable computers today, nature still has the best engineering designs. For example, it is not coincidence that the nose of an airplane is very similar to that of a dolphin. Not only the physical appearance but also the behavior of animals in nature are opening the doors for new theories. This chapter shows that nature is still helping humans make the most efficient and brilliant engineering designs.

Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization

This study presents a novel fuzzy adaptive controller comprising a fuzzy direct model reference mechanism to control uncertain nonlinear SISO systems. The proposed method benefits from a reference model which is itself fuzzy. Since the reference model is fuzzy it has more degrees of freedom to define a more appropriate dynamic behavior for the system to be controlled. The flexibility caused by the fuzzy reference model makes it possible for the system to outperform the case when the reference signal is linear in terms of rise time and settling time. In addition, using the proposed approach, tracking error of the reference system reduces significantly. The direct method of Lyapunov is used to prove the stability of the system. The proposed method is tested on a Duffing oscillator subject to disturbances.

Adaptive direct fuzzy control of SISO nonlinear systems using a fuzzy reference model

A Novel Direct Model Reference Fuzzy Control Approach Based on Observer and Its Applications

In this paper, we present a new method of interval fuzzy model identification. Unlike the previously introduced methods, this method uses recursive least square methods to estimate the parameters. The idea behind interval fuzzy systems is to introduce optimal lower and upper bound fuzzy systems that define the band which contains all the measurement values. This results in lower and upper fuzzy models or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). This type of modeling has various applications such as nonlinear circuits modeling. There has been tremendous amount of activities to use linear matrix inequality based techniques to design a controller for this type of fuzzy systems. The fact that the actual desired data must lie between upper and lower fuzzy systems, introduces some constrains on the identification process of the lower and upper fuzzy systems. We would introduce a cost function which includes the violation of constrains and try to find an adaptation law which minimizes this cost function and at the same time tries to be less conservative.

Mojtaba Ahmadieh Khanesar

Papers

Stabilization of type-2 fuzzy Takagi-Sugeno-Kang identifier using Lyapunov functions

Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization

Adaptive direct fuzzy control of SISO nonlinear systems using a fuzzy reference model

A Novel Direct Model Reference Fuzzy Control Approach Based on Observer and Its Applications

Identification of interval fuzzy models using recursive least square method