Top PDF Velocity control of a unicycle type of mobile robot using optimal pid controller

Velocity control of a unicycle type of mobile robot using optimal pid controller

Velocity control of a unicycle type of mobile robot using optimal pid controller

𝑢 and 𝜔 as the controller parameters. Tracking is much easier in this model. In this paper, the dynamic of the robot parameter is controlled using two blocks of Proportional-Integral-Derivative (PID) controllers. The gains of the PID are firstly determined using particle swarm optimization (PSO) in offline mode. After the optimal gain is determined, the tracking of the robot’s trajectory is performed online with optimal PID controller. The achieved results of the proposed scheme are compared with those of dynamic model optimized with genetic algorithm (GA) and manually tuned PID controller gains. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error (IAE) performance index. The simulation results obtained reveal advantages of the proposed PSO-PID dynamic controller for trajectory tracking of a unicycle type of mobile robot. A MATLAB-Simulink program is used to simulate the designed system and the results are graphically plotted. In addition, numerical simulations using 8-shape as a reference trajectory with several numbers of iterations are reported to show the validity of the proposed scheme.
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Development Of A 3-Axis Articulated Type Robot Arm With A Mobile PID Controller

Development Of A 3-Axis Articulated Type Robot Arm With A Mobile PID Controller

A 3-axis articulated robot represents an articulated robot with 3 degree of freedom (DOF). There is 3 independent ways the robot can move without violating any constraint imposed on it, stated by Ganesan A at el. (2015). by referring research paper of Mohammad Jived Ansari (2014) a 3 DOF articulated robot used 3 motor, the number of DOF is equal to the number of motor. The control of the robot achieved using microcontroller ATmega8A. The robotic arm is respond to gesture and can be programmed to move in desire path. These designs contain 2 mode that is shadow mode and automatic mode. The design help improve the function of robotic arm in various field included medicine, education, military, research and manufacturing. Another journal prepared by Gamesman A et al. (2015). Implemented LabVIEW and my-RIO to 3-axis articulated robot control. This idea opens a new variation to control an articulated robot. The processer used in the research is NI-myRIO that is capable to acquire and process real time signals, providing connection between the computer and the robotic arm.
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PSO based Optimum Design of PID Controller for Mobile Robot Trajectory Tracking

PSO based Optimum Design of PID Controller for Mobile Robot Trajectory Tracking

Let us consider the kinematic model (study of the mathematics of motion without considering the forces that affect the motion , it deals with the geometric relationships that govern the system and deals with the relationship between control parameters and the behavior of a system in state space [8]) for an autonomous vehicle. The position of the mobile robot in the plane is shown in Figure 1, the inertial-based frame (Oxy) is fixed in the plane of motion and the moving frame is attached to the mobile robot. The mobile robots are rigid cart equipped, with non-deformable conventional wheels, and it is moving on a non-deformable horizontal plane. During the motion: the contact between the wheel and the horizontal plane is reduced to a single point, the wheels are fixed, the plane of each wheel remains vertical, the wheel rotates about its horizontal axle and the orientation of the horizontal axle with respect to the cart can be fixed [9]. This means that the velocity of the contact point between each wheel and the horizontal plane is
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Eigen Value Analysis of Optimal Controller Design of Wheeled Autonomous Mobile Robot

Eigen Value Analysis of Optimal Controller Design of Wheeled Autonomous Mobile Robot

Recently mobile robot has been one of the central subjects in the research and development arena in the field of autonomous agents. They have been extensively applied in service industry, surveillance, geographical survey, remote access of dangerous location around the world as well as in domestic needs some of the many aspects which are typically studied in mobile robotics are path planning, trajectory tracking and controller stabilization. Non-holonomic robot is a popular differential drive mobile robot which is used in research as well as industrial applications [12]. However, few problems are associated with this type of robot is its high speed motion and hence the difficulty in avoiding actuator velocity saturation[13]. This difficulty is overcome by modifying the trajectory tracking error appropriately[16,17]. According to the design criteria, the control law is defined such as to reduce the difference between future trajectory tracking error of the robot and the reference one. The other area of interest for researchers is to achieve the shortest path length of robot trajectory. In [11], a randomized planner is applied to surveillance robot to get optimal path. A differential drive mobile robot is applied to the area of defense and security patrolling [12,13] in which sensor signals are mapped into actuator response using behavioral architecture. This regulates both translational and rotational movements of the robot. A discussion of different steering controls where PWM is applied to control DC motor for stable navigation strategy is presented in[14].
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Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

Particle Swarm Optimization (PSO) technique, proposed by Kennedy and Eberhart is an evolutionary - type global optimization technique developed due to the inspiration of social act ivities in flock of birds and school of fish and is widely applied in various engineering problems due to its high computational efficiency . It has been proved to be an effective optimum tool in system identification and PID controller tuning for a class of processes. This techniques is used to minimize the maximum overshoot, minimize the rise time, minimize speed tracking error, minimize the steady state error, and minimize the settling time, optimization solution results are set of near optimal trade-off value which are called the Pareto front or optimally surfaces. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. The components of PSO are Swarm Size, Velocity, position components and maximum no of iteration. The structure of the PID controller with PSO algorithm is shown in Fig.3.
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Two wheeled balancing robot controller designed using pid

Two wheeled balancing robot controller designed using pid

The linear controllers are more popular among researcher designing similar balancing robots like JOE [7]. Linear state space controllers like the Pole placement controller and the Linear Quadratic Regulators (LQR) are the two most popular control system implemented. In the paper of Two Wheels Mobile Robot Using Optimal Regulator Control, N. M. Abdul Ghani [15] mentioned that Pole placement gives best performance in term of settling time and magnitude for position, speed, angle and angle rate of two wheels mobile robot. A comparison of the results has demonstrated that Pole placement control provide higher level of disturbance reduction as compared to LQR technique.
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Development of self balancing platform on mobile robot using PID controller

Development of self balancing platform on mobile robot using PID controller

Control systems are often designed to improve stability, speed of response, steady- state error, or prevent oscillations. Many researchers wants to produce a mathematical equation that is able to determine the position of a very accurate motor position, thus the steady state error should be zero. DC motor systems have played an important role in the improvement and development of the industrial revolution. Therefore, the development of a more efficient control strategy that can be used for the control of a DC servomotor system and a well defined mathematical model that can be used for off line simulation are essential for this type of systems. Servomotor systems are known to have nonlinear parameters and dynamic factors, so to make the systems easy to control, conventional control methods such as PID controllers are very convenient. Also, the dynamics of the servomotor and outside factors add more complexity to the analysis of the system, for example when the load attached to the control system changes.
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Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

In the integer PID controller, the real order for the derivation and integration that we want to control are both unity. But in fractional-order PID controllers, a fractional order was used in the integration and differentiation parts of this controller to improve the conventional PID controller(Buniyamin, 2011). The P is an expansion of traditional PID controller with a new integral order and a new derivative order have fractional values that let the system less sensitive to the change in parameters and better control of dynamic systems (Ameer, 2014; Mouwafak, 2014). The differential equation of the P controller can be represented as follows:
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Design an Optimal PID Controller using Artificial Bee Colony and Genetic Algorithm for Autonomous Mobile Robot

Design an Optimal PID Controller using Artificial Bee Colony and Genetic Algorithm for Autonomous Mobile Robot

Mobile robot manipulators are mobile robot bases with at least one mounted robot arm which function in an integrated manner. The purpose of the mobile manipulator is to reach concrete locations in its environment and to pick up objects. There are two applications of using a mobile robot manipulator (MRM). The first one: using the MRM in unstructured environments, especially in the scenario that is unsuitable for human beings. The second application: using the MRM to transport objectives and tools in an already known industrial environment. Autonomous mobile robots are able to carry out many functions in dangerous sites where humans cannot reach, such as sites where harmful gases or high temperature are present, a hard environment for humans. Home assistant robots are expected to support daily activities at home. In all these examples robots have to move to their destination in order to perform their functions. For this purpose they need to be able to recognize the changes of environment using various sensors and cameras, and be equipped with a motion planning method in order to avoid collision with obstacles or other robots. In this work, the target tracking control issue are studied to improve the performances of the mobile robot.
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Design and modelling of 6 DOF revolute robot using fuzzy PID controller

Design and modelling of 6 DOF revolute robot using fuzzy PID controller

In the field of robot manipulators, many researchers have proposed literatures and discussed the kinematics analysis of industrial robots such as SCARA, PUMA 560 and SG5-UT robot manipulator [7], [19]. Other papers discussed about the control techniques problems such as PID, FLC, neural network algorithm and also the combination of the three controllers. Some of the research employed the Denavit Hartenberg method which it is used to model the mathematical equation of kinematics [11]. The forward and inverse kinematic equation analysis was generated and implemented using a simulation program [20], [21].
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Trajectory Following of an Industrial Robot using Optimized Fractional Order PID Controller

Trajectory Following of an Industrial Robot using Optimized Fractional Order PID Controller

The dynamics of the robot consist of two parts, the direct and inverse dynamics [1]. The purpose of direct dynamics is to gain the momentum, velocity and acceleration of the robot tool holding forces. Also torques applies to the joints or is irritates, but the inverse dynamic modeling with knowledge of routes, velocities and accelerations of the robot tool, or momenta forces driving the joints are calculated. Among classical methods for the calculation of dynamic robot models are Lagrange, D'Alember method, Newton, Euler equations, virtual work and Hamilton [2]. Nowadays, most robots used need to work quickly and efficiently. Among uses of robots are use in assembly lines, medical, machining and many other applications mentioned. The use of industrial robots in the production process and automation industry has grown considerably in recent decades. Most machine tool spindle apparatus are used for the series chain kinematics. Due to the widespread use of dynamic structures, control is vital to the comment [3].
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Direct torque control of IM using PID controller

Direct torque control of IM using PID controller

In this paper, the proposed algorithm of DTC of an IMD is investigated by using simulation in software program. In this work a several tests perform on the system so as to examination the performance characteristic of the proposed classical DTC system and DTC with PID controller. Note that at time of load change the torque and speed are not response smoothly with traditional DTC, but get better response when using PID controller in addition to the ripple in the torque is minimized. The response of the stator current is shown in Figure 7, therefor the IMD system will produce the magnetic flux as in Figure 8, and hence the rotor speed and electromagnetisms torque response of classical DTC at no load and different load disturbance is shown in Figure 9.
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Optimal Design of PID Controller for a CSTR System Using BF PSO

Optimal Design of PID Controller for a CSTR System Using BF PSO

[17]. Practical experiences suggest that they reach stagnation after certain number of generations as the population is not converged locally, so they will stop proceeding towards global optimal solutions. The stochastic search methods are proven in reaching global solutions for certain difficult real world optimization problems [18]. Hence this article comes up with a hybrid approach involving PSO-DE and BFOA algorithm for solving non-convex DED problem considering valve-point loading effects, ramp-rate limits, prohibited operating regions and spinning reserve capacity.
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Optimal PID Controller Design for AVR System

Optimal PID Controller Design for AVR System

( ) ) and the best output response in 50 iteration are shown in Figure 5. From the simulation and comparison results, we can find that the new fitness function defined by Equation (32) helps the RGA and PSO algorithm to search a best solution more accurate than the fitness function defined by Equation (31). The PID control parameter set selected by the RGA and PSO algorithm in 50 iterations for the AVR system based on the fitness function defined by Equation (31) and Equation (32) with b = 1.5 are respectively described in Table 6 and Table 7. Comparison of the control perfor- mance of the AVR system controlled by different con- trollers described in Table 6 ( b = 1.5 and f
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Particle Swarm Optimization Based Tuning of PID Controller for Robot Arm Joint Control

Particle Swarm Optimization Based Tuning of PID Controller for Robot Arm Joint Control

Where n is positive integer and T is the sampling period. y(nT), e(nT), r(nT) and a(nT) denote process output, error, rate and acc at sampling time nT, respectively. GE (gain for error) is the input scalar for rate, GA (gain for acc) the input scalar for acc and GU (gain for controller output) the output scalar of the FLC. F(.) describes the fuzzification of the scaled output of the FLC at sampling time nT.dUi(nT) (i=1,2) designate the incremental output of the fuzzy control block i from the defuzzification of the fuzzy set ‘output i’ ui~(nT) at sampling time nT. Thus the FLC includes the following components.
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Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

Abstract. This paper investigates the use of fractional order Proportional, Integral and Derivative (FOPID) controllers for the frequency and power regulation in a microgrid power system. The proposed microgrid system composes of renewable energy resources such as solar and wind generators, diesel engine generators as a secondary source to support the principle generators, and along with different energy storage devices like fuel cell, battery and flywheel. Due to the intermittent nature of integrated renewable energy like wind turbine and photovoltaic generators, which depend on the weather conditions and climate change this affects the microgrid stability by considered fluctuation in frequency and power deviations which can be improved using the selected controller. The fractional-order controller has five parameters in comparison with the classical PID controller, and that makes it more flexible and robust against the microgrid perturbation. The Fractional Order PID controller parameters are optimized using a new optimization technique called Krill Herd which selected as a suitable optimization method in comparison with other techniques like Particle Swarm Optimization. The results show better performance of this system using the fractional order PID controller-based Krill Herd algorithm by eliminates the fluctuations in frequency and power deviation in comparison with the classical PID controller. The obtained results are compared with the fractional order PID controller optimized using Particle Swarm Optimization. The proposed system is simulated under nominal conditions and using the disconnecting of storage devices like battery and Flywheel system in order to test the robustness of the proposed methods and the obtained results are compared. References 18, figures 8.
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Optimal Wastewater Treatment Plant By PID Controller

Optimal Wastewater Treatment Plant By PID Controller

PID is the most common control algorithm used in process industry and wastewater treatment. PID structured is very simple and the control principle is very clear. In wastewater treatment, parameters were tuned once only at the beginning of the installation. The actual control system design will consider the control structure, control algorithm and tuning the controllers. Basically, the system of the plant will use closed loop control systems to reduce the disturbance and the sensitivity to parametric uncertainty. The PID parameters that need to be determined were shown in the equation below where the value of Kp, Ki, and Kd is needed in order to design controller.
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Enhancement of Control Signal Using a Modified Parallel PID Controller

Enhancement of Control Signal Using a Modified Parallel PID Controller

Figure 1. The controller and the process connection As shown from Figure1, the first part of the plant called an actuator or final control element which receive the control signal from the controller and adjust the output to keep it at the desired value of the control system, physically many problems occurs at the controller and actuator connection. In the parallel PID form shown in Figure2. These problems are related with its blocks or components which can be summarized in the following sections .

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Load Frequency Control for Hydropower Plants using PID Controller

Load Frequency Control for Hydropower Plants using PID Controller

conventional energy and towards to use renewable energy like hydropower system, solar cells and wind turbines as soon as possible. Load Frequency Control (LFC) problem is coming to be the main topics for mentioning schemes due to not corresponding between main power system inputs such as change load demand and change in speed turbine settings. This paper illustrates a self- tuning control of hydropower system that suggested and confirmed under Automatic Generation Control (AGC) in power scheme. The suggested power system involves one single area. The suggested self-tuning control system is employed in performing the automatic generation control for load frequency control request and compared it with conventional control structure. The power system dynamic modeling has regularly built in several essential parameters which have a significant influence According to frequency limitation. The main problem with all controllers is an exaggerated reaction to minor errors, producing the system to oscillate. The output response results for hydropower system obviously proved the benefit of using maximum load demand by tuning PID controller. Whereas, tuning PID controller has got properly more rapid output response and minimal overshoot.
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Robotic Arm Control Using Pid Controller And Inverse Kinematics

Robotic Arm Control Using Pid Controller And Inverse Kinematics

The benefits of on-orbit satellite servicing include satellite refuelling, satellite life extension, debris removal, repair and salvage. The robotic systems play an important role in satellite servicing and satellite capture is a critical phase for enabling service operations. In the satellite operations the servicing vessel approaches the target satellite to a distance .Then a robotic manipulator is used to autonomously capture the target satellite and perform the docking operation with the vehicle. Movements of the manipulator disturb the attitude of the satellite carrying it, complicating the kinematics and dynamics analysis of the space manipulator. The workspace is reduced as well. As regards the base spacecraft, three types of operation are considered. The first type corresponds to the free flying case, where the base is actively controlled and hence, the entire servicing system is capable of being transferred and orientated arbitrarily in space. The utilization of such a system may be limited because the manipulator motion can both saturate the reaction jet system and consume large amount of fuel. In the second type, the base attitude is controlled by using reaction wheels, leaving the spacecraft only in free translation.
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