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Go to Editorial ManagerThe goal of this paper is to present a study of tuning the Proportional-Integral-Derivative (PID) controller for control the position of a DC motor by using the Particle Swarm Optimization (PSO) technique as well as the Ziegler & Nichols (ZN) technique. The conventional Ziegler & Nichols (ZN) method for tuning the PID controller gives a big overshoot and large settling time, so for this reason a modern control approach such as particle swarm optimization (PSO) is used to overcome this disadvantage. In this work, a third order system is considered to be the model of a DC motor. Four types of performance indices are used when using the particle swarm optimization technique. These indices are ISE, IAE, ITAE and ITSE. Also study the effect of each one of these performance indices by obtaining the percentage overshoot and settling time when a unit step input is applied to a DC motor. A comparison is made between the two methods for tuning the parameters of PID controller for control the position of a DC motor is considered. The first one is tuning the controller by using the Particle Swarm Optimization technique where the second is tuning by using the Ziegler & Nichols method. The proposed PID parameters adjustment by the Particle Swarm Optimization technique showed better results than the Ziegler & Nichols’ method. The obtained simulation results showed good validity of the proposed method. MATLAB programming and Simulink were adopted in this work.
Mobile robots use simultaneous localization and mapping (SLAM) techniques for generating maps of unknown environments through navigating its. In this work, firstly SLAM technique was considered based on extended Kalman filter (EKF) which it was implemented and evaluated at unknown environments with different number of landmarks to estimate mobile robot’s position and build a map for navigated environment at the same time. Then, the detectable landmarks will play an important role in controlling the overall navigation process as well EKF-SLAM technique’s performance. After that, three intelligent optimization algorithms are proposed to enhance the performance of the EKF-SLAM trajectory for the mobile robot, these algorithms are: particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO) and genetic optimization (GA). MATLAB simulation results show that CPSO algorithm outperforms PSO and GA algorithms in terms of minimizing the mean square error (MSE1) with increasing the number of landmarks, where MSE1 is the mean square error of EKF-SLAM according to the actual trajectory. The simulation results show also the performance of EKF-SLAM trajectory is better than the performance of the Odometry trajectory and becomes best with using intelligent optimization algorithms.
This paper provides a theoretical framework for analysis of consensus algorithm for multi-agent networked systems considering the role of directed information flow. Improvement of the performance of the implemented consensus algorithm has been achieved by using Particle Swarm Optimization (PSO). Concepts of information consensus in networks and methods of convergence are applied as well. Our analysis framework is based on tools algebraic Graph Theory (GT). Simulation of multi-agent system and the performance of a consensus algorithm have been discussed. Acceleration the network while approaching the required goal has been accomplished and elimination of undesired swing that appears during the acceleration was proved.
In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.
This paper presents the design of robust four parameters (two degree of freedom) PI-PD controller based on Kharitonov theorem for antilock braking system. The Particle Swarm Optimization (PSO) method is used to tune the parameters of the proposed controller based on Kharitonov theorem to achieve the robustness over a wide range of system parameters change. The proposed cost function combines the time response specifications represented by the model reference and the frequency response specifications represented by gain margin and phase margin and the control signal specifications. The model reference control is used because of the antilock braking system is originally nonlinear and has different operating points. The robust stability is guaranteed by applying the Kharitonov theorem. Three types of road conditions (dry asphalt, gravel and icy) are used to test the proposed controller.