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

This paper provides a mathematical analysis that shows how the crisp output of an IT2 FLS that is obtained by using the Begian-Melek-Mendel (BMM) formula compares to the one obtained by using center-of-sets type-reduction followed by defuzzification (COS TR + D). This is made possible by reformulating the structural solutions of the two optimization problems that are associated with COS TR, and then expanding each of them using a Maclaurin series expansion. As a result of doing this, we show that BMM is the zero-order approximation to COS TR + D. Additionally, by retaining the zero-order and first-order terms from the Maclaurin series expansions, we provide a new Enhanced BMM, one that is non-iterative, has a closed form and is much faster than using the EKM algorithms for COS TR. Although the Enhanced BMM formula is slower than BMM, we demonstrate, by means of extensive simulations, that it is from 5% to 50% more accurate than is BMM for achieving the same numerical solution that is obtained from COS TR + D; and, it is at least 94% faster than when EKM is used for COS TR +D, which makes the Extended BMM a very strong candidate for use in real time applications of IT2 FLSs.

Maclaurin series expansion complexity-reduced center of sets type-reduction + defuzzification for interval type-2 fuzzy systems

This paper presents a recurrent interval type-2 neuro-fuzzy controller which benefits from a sliding mode theory-based training algorithm. The recurrent interval type-2 neuro-fuzzy benefits from recurrent type-2 membership functions with interval variances which are trained by a novel training method. Furthermore, the adaptation laws considered for the parameters of the controller benefit from an adaptive learning rate. The stability of the proposed training method is considered using an appropriate Lyapunov function. The proposed method is simulated on an electro hydraulic servo system. The results of simulations show that the proposed method can control the system with a satisfactory performance.

Recurrent interval type-2 neuro-fuzzy control of an electro hydraulic servo system

In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed, Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

Direct Stable Adaptive Fuzzy Neural Model Reference Control of a Class of Nonlinear Systems

This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

https://www.mdpi.com/1996-1073/15/5/1876/pdf?version=1646410283

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

This paper proposes an online tuning method for the parameters of a fuzzy neural network using variable structure systems theory. The proposed learning algorithm establishes a sliding motion in terms of the fuzzy neuro controller parameters, and it leads the error towards zero. The Lyapunov function approach is used to analyze the convergence of the weights for the case of triangular membership functions. Sufficient conditions to guarantee the convergence of the weights are derived. In the simulation studies, the approach presented has been tested on the velocity control of an electro hydraulic servo system in presence of flow nonlinearities and internal friction.

Mojtaba Ahmadieh Khanesar

Papers

Maclaurin series expansion complexity-reduced center of sets type-reduction + defuzzification for interval type-2 fuzzy systems

Recurrent interval type-2 neuro-fuzzy control of an electro hydraulic servo system

Direct Stable Adaptive Fuzzy Neural Model Reference Control of a Class of Nonlinear Systems

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

An online training algorithm based on the fusion of sliding mode control theory and fuzzy neural networks with triangular membership functions