Engineering and Technology Journal

The aim of this paper is to design a proposed non-linear fractional order proportional-integral-derivative neural (NFOPIDN) controller by modifying and improving the performance of fractional order PID (FOPID) controller through employing the theory of neural network with cognitive optimization techniques for the differential - drive wheeled mobile robot (WMR) multi-input multi-output (MIMO) system in order to follow a pre-defined trajectory. In this paper a cognitive bacterial foraging optimization algorithm (BFOA) has been utilized to find and tune the parameters of the proposed (NFOPIDN) controller and then find the optimal torque control signals for the differential - drive WMR. The simulation results show that the proposed controller can give excellent performance in terms of compared with other works (minimized tracking error for Ranunculoid-curve trajectory, smoothness of torque control signals obtained without saturation state and no sharp spikes action as well as minimum number of memory units needed for the structure of the proposed NFOPIDN controller).

The goal of this paper is to design a proposed non-linear fractional order proportional-integral-derivativeneural (NFOPIDN) controller by modifying and improving the performance of fractional order PID (FOPID) controller through employing the theory of neural network with optimization techniquesfor the differential wheeled mobile robotmulti-input multi-output (MIMO) systemin order to follow a desired trajectory. The simplicity and the ability of fast tuning are important features of the particle swarm optimization algorithm (PSO) attracted us to use it to find and tune the proposed non-linear fractional order proportional-integral-derivative neural controller’s parameters and then find the best velocity control signals for the wheeled mobile robot. The simulation results show that the proposed controller can give excellent performance in terms of compared with other works (minimized mean square error equal to 0.131 for Eight-shaped trajectory and equal to 0.619 for Lissajous- curve trajectory as well as minimum number of memory units needed for the structure of the proposed NFOPIDN controller (M=2 for Eight-shaped trajectory and M=4 for Lissajous- curve trajectory) with smoothness of linear velocity signals obtained between (0 to 0.5) m/sec.

This paper investigates the improvement of the speed and torque dynamic responses of three phase Permanent Magnet Synchronous Motor (PMSM) using Direct Torque Control (DTC) technique. Different torques are applied to PMSM at different speeds during operation to ensure the robustness of the controller for wide torque variations. Optimal PI controller is used to modify the response of DTC. The optimal gains of PI controller are tuned by Particle Swarm Optimization (PSO) technique. Neural Network controller is called the Nonlinear Autoregressive-Moving Average (NARMA-L2) which is trained based on optimal PI controller (PI-PSO) data. The results show the superiority performance of using NARMA-L2 controller on PI-PSO controller for different speeds and load change. The overall simulation and design of the scheme are implemented Using MATLAB/Simulink program.

This paper presents an application of Fuzzy Logic Control (FLC) in the separately excited Direct Current (DC) motor drive (SEDM) system; the controller designed according to Fuzzy Logic rules. Such that the system is fundamentally robust. These rules have capability learning, can learn and tune rapidly, even if the motor parameters are varied. The most commonly used method for the speed control of dc motor is Proportional- Integral- Derivative (PID) controller. Simulation results demonstrate that, the control algorithms Neuro-Fuzzy logic and PID, the dynamic characteristics of the SEDM (speed, torque, as well as currents) are easily observed and analyzed by the developed model. In comparison between the Neuro-fuzzy logic controller and PID controller, the FLC controller obtains better dynamic behavior and superior performance of the DC motor as well as perfect speed tracking with no overshoot, and the proposed controller provides high performance dynamic characteristics and is robust with regard to change of motor speed and external load disturbance. This paper also discusses and compares the speed control systems of SEDM using PID- controller conventional and Fuzzy Logic-controller. The entire system has been modeled using MATLAB 10a/SIMULINK toolbox.