Control
Hadeel I. Abdulameer; Mohamed J. Mohamed
Abstract
Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative ...
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Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative and Integer Order Fuzzy Proportional Integral Derivative controllers is evaluated for reference trajectory tracking, changing beginning circumstances, disturbance rejection, and model uncertainty. These controllers' parameters are tuned using a meta-heuristic optimization approach called the most valuable player algorithm for the objective function, which is defined as the integral of the time-squared error. Simulation results show that the suggested Fractional Order Fuzzy Proportional Integral Derivative controllers outperform Integer Order Fuzzy Proportional Integral Derivative controllers for tracking performance, stability, and robustness for all structures. Fractional Order Fuzzy Proportional Derivative Fractional Order Proportional Integral Derivative controller is the best one for trajectory tracking, disturbances rejection, and parameter variation with the least integral of time square error equal to 2.7420×10-6, 3.4×10-3 and 2.0108×10-4 respectively and the response of the angular position for all links for trajectory tracking has minimum settling time which is equal to 0.0290 s for the first link, 0.0160 s for the second link and 0.0050 s for the third link. When the initial condition is changed, the One Block Fractional Order Fuzzy Proportional Integral Derivative controller is the best one, since the integral of time square error is minimum and equal to 1.6253×10-4.
Wajdi T. Joudah Al-Rubaye; Ah med Al-Araji1; Hayder A. Dhahad
Volume 20, Issue 3 , July 2020, , Page 50-64
Abstract
This paper proposes an off-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. The aim of this research is to obtain the best hydrogen partial pressure (PH2) value using FPGA ...
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This paper proposes an off-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. The aim of this research is to obtain the best hydrogen partial pressure (PH2) value using FPGA emulator to design and implement a digital PID controller that track the fuel cell output voltage during a variable load current applied. The off-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID controller parameters that improve the dynamic behavior of the closed loop digital control fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package and FPGA emulator experimental work show the performance of the proposed FPGA-PID controller in terms of voltage error reduction and generating optimal value of the (PH2) control action without oscillation in the output and no saturation state when these results are compared with other control methodology.
Ahmed S. Al-Araji1; Attarid K. Ahmed
Volume 18, Issue 2 , September 2018, , Page 1-16
Abstract
This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation ...
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This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation through static obstacles. Theproposed cognitive system consists of two parts: the first part is to plan the desired path for themobile robot with the static obstacle environment in order to determine the target point and toavoid the obstacles based on the proposed square road map algorithm. The second part is toguide and track the wheeled mobile robot on the desired path equation based on the proposednonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle SwarmOptimization (PSO) is used to on-line tune the variable control parameters of the proposedcontroller to get the optimal torques actions for the mobile robot platform. Based on using theMATLAB package (2017), the numerical simulation results show that the proposed cognitivesystem has high accuracy for planning the desired path equation in terms of avoiding the staticobstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m)without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error forthe mobile robot to the zero value approximation. These results were confirmed by acomparative study with different nonlinear PID controller types in terms of number ofiterations and the performance index.
Hussein S. Mohammed; Bashar F. Midhat; Firas A. Raheem
Abstract
This paper addresses the problem of position control and stabilization for the two wheeled balancing robot. A mathematical model is derived based on the robot’s position and tilt angle and a fuzzy logic control is proposed for the balancing robot control. The fuzzy logic controller performance ...
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This paper addresses the problem of position control and stabilization for the two wheeled balancing robot. A mathematical model is derived based on the robot’s position and tilt angle and a fuzzy logic control is proposed for the balancing robot control. The fuzzy logic controller performance is compared with a conventional PID controller to show the difference between them. Both controllers were tested on the balancing robot in simulation using MATLAB software and the results were put together for a comparative point of view. The simulations shows a relative advantage for the fuzzy logic controller over the conventional PID controller especially in reducing the time required for stabilization which takes about 2 seconds and almost without overshoot while in the PID case the robot will have about 10% overshoot in position and about 20 degrees in tilt angle.
Khulood E. Dagher
Volume 13, Issue 3 , December 2013, , Page 1-9
Abstract
Abstract – This paper introduces the Slice Genetics Algorithm SGA which represents
the proposed modification to the classic Genetic Algorithm GA scheme. The proposed
algorithm has reduced the population size and maximum iteration in order to get fast
and an optimal solution. This algorithm has been ...
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Abstract – This paper introduces the Slice Genetics Algorithm SGA which represents
the proposed modification to the classic Genetic Algorithm GA scheme. The proposed
algorithm has reduced the population size and maximum iteration in order to get fast
and an optimal solution. This algorithm has been used for determining the optimal
proportional- integral- derivative PID controller parameters. The proposed algorithm
has versatile features, including, fast, stable rate convergence characteristic also it has
good computational efficiency in improving the dynamic behavior for the system in
term of reducing the maximum overshoot, rise time, settling time and steady-states
error. The algorithm not only has benefit to improve the convergence characteristic,
accuracy but it also shortened the processing time towards the optimal value based
reducing the number of iteration from 40 to 4 or 6 iteration as clear in the MATLAB
simulation results..
Dr. Farooq Al- doraiee; Salih Al-Qaraawi; Hassan J. Hassan
Volume 13, Issue 1 , April 2013, , Page 9-17
Abstract
Abstract – Internet represents a shared resource wherein users contend for the finite
network bandwidth. Contention among independent user demands can result in
congestion, which, in turn, leads to long queuing delays, packet losses or both.
Congestion control regulates the rate at which traffic ...
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Abstract – Internet represents a shared resource wherein users contend for the finite
network bandwidth. Contention among independent user demands can result in
congestion, which, in turn, leads to long queuing delays, packet losses or both.
Congestion control regulates the rate at which traffic sources inject packets into a
network to ensure high bandwidth utilization while avoiding network congestion. In the
current Internet, there are two mechanisms which deal with congestion; the end-to-end
mechanism which is achieved by the Transmission Control Protocol (TCP) and the
intermediate nodes algorithms such as Active Queue Management (AQM) in routers.
In this paper, a combined model of TCP and AQM (TCP/AQM) is formulated and
first simulated without a controller. The results show that it is unable to track the desired
queue size. So, to get better tracking performance, an adaptive PID controller based on
wavelet network (AWNPID) is used as AQM in the router queue. The non-adaptive PID
controller is also demonstrated, and its weakness to the network dynamic changes is
compared to the robustness of adaptive controller (AWNPID). The analytical results for
linearized TCP/AQM model are presented in MATLAB version 7.0.
Salih M. Attya; Dr. Mohammed H. Al-Jammas; Dr. Mazin Z. Othman
Volume 12, Issue 2 , December 2012, , Page 90-97
Abstract
Abstract-- In this work, the power of the Genetic Algorithms (GA) in searching for an optimal solution (in a pre-determined hyper space) is used to design the suitable configuration and parameters of the Proportional-Integral-Derivative (PID) controller. In most industrial plants, the PID controllers ...
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Abstract-- In this work, the power of the Genetic Algorithms (GA) in searching for an optimal solution (in a pre-determined hyper space) is used to design the suitable configuration and parameters of the Proportional-Integral-Derivative (PID) controller. In most industrial plants, the PID controllers are configured either in cascade, feedback or in feed forward topologies. Besides, for each of these configurations the tuning gains have to be fixed in order to meet the required specifications. Therefore, GA is utilized efficiently to select the proper PID configuration in the context of signal following approach as well as the best tuning gains for the selected configuration. The proposed design procedure is applied to linear and nonlinear plants. It reflects a tremendous design results that heavily relied on computer to get the required controller.
