Farzad Hashemzadeh

Bio: Farzad Hashemzadeh is an academic researcher from University of Tabriz. The author has contributed to research in topics: Control theory & Teleoperation. The author has an hindex of 13, co-authored 73 publications receiving 1044 citations. Previous affiliations of Farzad Hashemzadeh include University of Alberta & University of Tehran.

Colonial competitive algorithm A novel approach for PID controller design in MIMO distillation column process

Abstract: Purpose – This paper aims to describe colonial competitive algorithm (CCA), a novel socio‐politically inspired optimization strategy, and how it is used to solve real world engineering problems by applying it to the problem of designing a multivariable proportional‐integral‐derivative (PID) controller Unlike other evolutionary optimization algorithms, CCA is inspired from a socio‐political process – the competition among imperialists and colonies In this paper, CCA is used to tune the parameters of a multivariable PID controller for a typical distillation column processDesign/methodology/approach – The controller design objective was to tune the PID controller parameters so that the integral of absolute errors, overshoots and undershoots be minimized This multi‐objective optimization problem is converted to a mono‐objective one by adding up all the objective functions in which the absolute integral of errors is emphasized to be reduced as long as the overshoots and undershoots remain acceptableFindin

Position and force tracking in nonlinear teleoperation systems under varying delays

Abstract: In this paper, a novel control scheme is proposed to guarantee position and force tracking in nonlinear teleoperation systems subject to varying communication delays. Stability and tracking performance of the teleoperation system are proved using a proposed Lyapunov–Krasovskii functional. To show its effectiveness, the teleoperation controller is simulated on a pair of planar 2-DOF (degree of freedom) robots and experimented on a pair of 3-DOF PHANToM Premium 1.5A robots connected via a communication channel with time-varying delays. Both the planar robots in simulations and the PHANToM robots in experiments possess nonlinear dynamics.

Adaptive Control

Abstract: 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.

Nonlinear System Identification

Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

A review of population-based meta-heuristic algorithm

Abstract: Exact optimization algorithms are not able to provide an appropriate solution in solving optimization problems with a high-dimensional search space. In these problems, the search space grows exponentially with the problem size therefore; exhaustive search is not practical. Also, classical approximate optimization methods like greedy-based algorithms make several assumptions to solve the problems. Sometimes, the validation of these assumptions is difficult in each problem. Hence, meta-heuristic algorithms which make few or no assumptions about a problem and can search very large spaces of candidate solutions have been extensively developed to solve optimization problems these days. Among these algorithms, population-based meta-heuristic algorithms are proper for global searches due to global exploration and local exploitation ability. In this paper, a survey on meta-heuristic algorithms is performed and several population-based meta-heuristics in continuous (real) and discrete (binary) search spaces are explained in details. This covers design, main algorithm, advantages and disadvantages of the algorithms.