Journal of Majlesi Journal of Mechatronic Systems
Volume:5 Issue: 1, Mar 2016

This paper presents an innovative active power filter design method to compensate simultaneously the current harmonics and reactive power of a nonlinear load. The power filter integrates a passive power filter, which is an RL low-pass filter placed in series with the load, and an active power filter, which comprises of an RL in series with an IGBT based voltage source converter. The filter is supposed to inject a current into the connection node of the load and grid to eliminate current harmonics and its reactive part. The voltage source converter is placed in a hysteresis feedback control loop to generate a harmonic current. The bandwidth and output amplitude of the hysteresis controller are optimized with the inductance of RL filters. Three objective functions are considered in the optimization problem, which include minimizing current total harmonic distortion, maximizing power factor, and minimizing the IGBT bridge current. For solving the optimization problem, four well-known multi-objective evolutionary algorithms are applied, namely, non-dominated sorting genetic algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Suppaptinarm Multiobjective Simulated Annealing Algorithm (SMOSA) and Multi-Objective Particle Swarm Optimization (MOPSO). The test results showed that the MOPSO technique exhibited the better performance against NSGA-II, SPEA2 and SMOSA relative to the objectives.

This paper is about an induction motor which can be utilized the amount of speeds in a desired range with particular torque. If chosen controller is designed with fuzzy logic, several efforts should have been done to improve the controller performance for correct control and quick response. Purpose of designing this controller, is providing stability and reducing overshoot in response to disturbances and sudden changes in particular speed reference.
In this paper, the method that is used for drive control is hybrid fuzzy control method, and is compared with the classic controllers and fuzzy controller. Advantages of this method are compared to other methods, that shows the ability of better control.
The performance of varies controllers are compared by MATLAB simulation. The results show satisfied performance of proposed method.

The proportional-integral derivative controller (PID Controller) is a control loop feedback mechanism (Controller) widely used in automatic process control applications in industry today to regulate flow, temperature, pressure, level, and many other industrial process variables. The high demand of PID controller is due to its fine control capabilities in a wide range of operating conditions. This paper presents design of PID controller using Ziegler–Nichols (ZN) technique for higher order systems. A fuzzy logic controller using simple approach and smaller rule set is also proposed. The aim of designed fuzzy controller is to present better control compared with the existing PID controller. The simulation is done using Matlab/Simulink by comparing the performance of two controllers for higher order systems. It is finally observed that fuzzy logic controller has better control on timing parameters such as settling time, rise time, maximum overshoot as compared to the existing PID tuning techniques.

In this paper, direct adaptive output feedback control schemes are developed to solve the robust Fault-Tolerant Control (FTC) problem for nonlinear Lipchitz systems with loss of actuator effectiveness and external disturbances. A class of robust adaptive output feedback controllers is constructed for automatically compensating the fault and the disturbance effects based on the information from the adaptive schemes. On the basis of Lyapunov stability theory, and with a constructive algorithm based on Linear Matrix Inequality (LMI) is presented for FTC design is shown that the resulting adaptive closed-loop system can be guaranteed to be asymptotically stable in the presence of faults on actuators and disturbances. The merit of purposed control scheme has been verified by the simulation on the quadruple tank process subjected to the actuator loss of effectiveness.

Recently due to limited resources and high cost of energy, optimization of energy consumption in many aspects is under study by researchers. The main purpose of this study is to optimize a robot with DOF to find the minimum energy consumption. At first, the kinematic, inverse kinematic and dynamic analysis of the robot is performed and then the energy equations of actuators are predicted. The default path of the robot is assumed as a five order polynomial and the consumption energy of the actuators for the motion of end-effector is obtained. For optimizing the path and comparing it to the default path, two random points in the workspace of the robot are selected and then five order spline in the robot workspace is defined. In addition, the continuity conditions are satisfied and the energy consumption for the new path is predicted. Finally, the genetic algorithm toolbox of MATLAB is applied for optimization. The energy function of the actuators are defined as the objective function and the workspace of the robot set as the guess range of spline points. Finally, the optimum path with minimum actuator energy consumption is obtained.

This paper presents a novel robust adaptive state dependent control strategy for the class of nonlinear Lipschitz systems in the presence of bounded matched or unmatched disturbances. A constructive algorithm based on Linear Matrix Inequalities (LMIs) by using Lyapunov stability theory is developed for on-line tuning of controller gains to stabilize the closed-loop control system asymptotically, and to attenuate disturbance effects. The resulting control system has simpler structure as compared with most existing methods. The merits of the proposed control scheme have been verified by the simulation on an active suspension system in the presence of physical parametric uncertainties.