PSO BASED TAKAGI-SUGENO FUZZY PID CONTROLLER DESIGN FOR SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR

A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority

Improve performance of fast steady state response for DC servo motor by fuzzy logic implementation

The DC Servo Motors has top position in various servomechanisms. It is important to study DC Servo motor along with its position control study. Generally transient responses of DC Servo Motor are improved by using PID controller. Currently, to provide workable initial value most tuning methods have been designed. The workable initial value further manually corrected for special requirement. The work presents fast tuning and flexible method which is based on Fuzzy logic. The GA (genetic algorithm) is used to determine some optimal parameter of PID controller. In this paper simulation results shows Fuzzy logic satisfied with wide range of requirement with compared to the tuning method. Fuzzy logic gives the actual response with respect to required response . DOI: 10.17762/ijritcc2321-8169.15068

Speed Control of Induction Motor using LQG

The electric motor is one of the technological developments which can support the production process. Not only in the manufacturing, but also in the transportation sector. The AC motor is divided into the synchronous and asynchronous motor. One type of asynchronous motor which widely used is the induction motor. In this study, the application of the IFOC control method and the LQG speed control method will be used to control the speed of an induction motor. The PID algorithm is also used as a comparison. Tests were carried out using MATLAB software. The speed variation and load variation are tested to validate the controller performance. PID is superior in terms of settling time and IAE. On the other hand, LQG is better in energy consumption. In terms of IAE, LQG has a higher value compared to PID by up to 56.67%. On the other hand, LQG is superior in terms of energy, which is 8.38% more efficient

Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

GA-Based Optimization for Multivariable Level Control System: A Case Study of Multi-Tank System

This paper presents a systematic way to determine the trade-off optimized controller tunings using computation optimization technique for both servo and regulatory controls of the Multi-Tank System, as one of the applications under the multivariable loop principle. The paper describes an improved way to obtain the best Proportional-Integral (PI) controller tunings in reducing the dependency on engineering knowledge, practical experiences and complex mathematical calculations. Relative Gain Array (RGA) calculation justified the degree of relation and the best pairing for both interacted control loops. Genetic Algorithm (GA), as one of the most prestigious techniques, was used to analyze the best controller tunings based on factor parameters of iterations, populations and mutation rates to the applied First Order plus Dead Time (FOPDT) models in the multivariable loop. Amid simulation analysis, GA analysis’s reliability was justified by comparing its performance with the Particle Swarm Optimization (PSO) analysis. The research outcome was visualized by generating the process responses from the LOOP-PRO’s multi-tank function, whereby the GA tunings’ responses were compared with the conventional tuning methods. In conclusion, the result exhibits that the GA optimization analysis has successfully demonstrated the most satisfactory performance for both servo and regulatory controls

Simulation Model of Servo Motor by Using Matlab

The research aims to develop documented empirical data to obtain a high-accuracy and effective system according to a principal system as a model that represents the system for all expected cases and different working conditions. The current works are simulating a servo motor that works with specifications as a mathematical representation of it down to its representation with a transformation function. The simulation is done for different cases, the first is without a controller, and the other is an operation simulation with a conventional controller that is with a PID controller. The results, through response and accuracy, prove the preference of PID controller systems in the speed of response and high accuracy with the change or different conditions of the system, i.e., working with linear systems. A simulation is being conducted to verify the use of control systems to improve the performance of servo motors. Algorithms of control systems are developed according to designs based on prior experience. Speed and position control are the most common and used in many applications, which created the need to choose them. To overcome fluctuations and obtain a quick response and a high-precision system used, control systems, as the results proved. The research contribution is developing a design for the user control systems also checking them in simulation with the servo motor system using MATLAB. They test them in the servo motor control as well to test their performance experimentally

Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor

DC motors are simple and controllable, making them a popular choice for various applications. However, the speed and load characteristics of DC motors can change, making it difficult to control them effectively. This paper proposes an optimum PID controller with fuzzy self-tuning for DC servo motors. The controller uses two steps to adjust the PID gains: The ACS algorithm is employed to identify the optimal PID gains in the first step. A fuzzy logic (FLC) controller is employed in the second stage to further fine-tune the gains. The FLC considers two cost functions: the first function is the sum of the squares of the error between the controlled output and reference input. The second function is a mathematical expression that specifies the required characteristics of the system response. The fuzzy self-tune then uses a set of rules to adjust the PID gains in response to changes in the system. The rules are based on the two cost functions designed to maintain the optimum PID gains for various operating settings. The outcomes of the two functions are: Kp = 5.2381, Ki = 7.0427, and Kd = 0.49468, with rising time = 0.2503, overshoot = 2.5079, and settling time = 10.4824 in the first cost function. The second cost function outcomes are Kp = 8.1381; Ki = 8.6427; and Kd = 0.49468. The FST-PID controller's performance is evaluated using Matlab-Simulink. The proposed controller was tested on a DC servo motor, and the results showed good performance in both steady-state and transient responses. The controller also maintained the optimum PID gains in the event of changes or disturbances. So, the motor's speed can effectively control under a variety of conditions

An Evolutionary Algorithm with Advanced Goal and Priority Specification for Multi-objective Optimization

This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical 'OR' and 'AND' connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line population distribution without needing any a priori parameter setting. Each feature in the proposed algorithm is examined to show its respective contribution, and the performance of the algorithm is compared with other evolutionary optimization methods. It is shown that the proposed algorithm has performed well in the diversity of evolutionary search and uniform distribution of non-dominated individuals along the final trade-offs, without significant computational effort. The algorithm is also applied to the design optimization of a practical servo control system for hard disk drives with a single voice-coil-motor actuator. Results of the evolutionary designed servo control system show a superior closed-loop performance compared to classical PID or RPT approaches

Multi-objective Optimization of Multi-loop Control Systems

Cascade Control systems are composed of inner and outer control loops. Compared to the traditional single feedback controls, the structure of cascade controls is more complex. As a result, the implementation of these control methods is costly because extra sensors are needed to measure the inner process states. On the other side, cascade control algorithms can significantly improve the controlled system performance if they are designed properly. For instance, cascade control strategies can act faster than single feedback methods to prevent undesired disturbances, which can drive the controlled system’s output away from its target value, from spreading through the process. As a result, cascade control techniques have received much attention recently. In this thesis, we present a multi-objective optimal design of linear cascade control systems using a multi-objective algorithm called the non-dominated sorting genetic algorithm (NSGA-II), which is one of the widely used algorithms in solving multi-objective optimization problems (MOPs). Two case studies have been considered. In the first case, a multi-objective optimal design of a cascade control system for an underactuated mechanical system consisting of a rotary servo motor, and a ball and beam is introduced. The setup parameters of the inner and outer control loops are tuned by the NSGA-II to achieve four objectives: 1) the closed-loop system should be robust against inevitable internal and outer disturbances, 2) the controlled system is insensitive to inescapable measurement noise affecting the feedback sensors, 3) the control signal driving the mechanical system is optimum, and 4) the dynamics of the inner closed-loop system has to be faster than that of the outer feedback system. By using the NSGAII algorithm, four design parameters and four conflicting objective functions are obtained. The second case study investigates a multi-objective optimal design of an aeroelastic cascade controller applied to an aircraft wing with a leading and trailing control surface. The dynamics of the actuators driving the control surfaces are considered in the design. Similarly, the NSGA-II is used to optimally adjust the parameters of the control algorithm. Ten design parameters and three conflicting objectives are considered in the design: the controlled system’s tracking error to an external gust load should be minimal, the actuators should be driven by minimum energy, and the dynamics of the closed-loop comprising the actuators and inner control algorithm should be faster than that of the aeroelastic structure and the outer control loop. Computer simulations show that the presented case studies may become the basis for multi-objective optimal design of multi-loop control systems