Structure and Synthesis of PID Controllers

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Ho, A. Datta and S. Bhattacharyya: A linear programming characterization of all stabilizing PID controllers, Proceedings of the American Control Conference , , pp. Hagiwara, K. Yamada, A. Hoang and S. All Rights Reserved. Log In. Paper Titles. Article Preview. Add to Cart. Key Engineering Materials Volume Main Theme:. Edited by:.

Sumio Hosaka. Online since:. December Cited by. Related Articles. Paper Title Pages. Abstract: On the basis of sufficient analysis to the characteristic of iterative learning control and PID controller parameter tuning, an idea of applying iterative learning control to PID controller parameter tuning was aroused in this paper, we choose the linear model around the character point of Roll Attitude controller as the research plant, transforming the problem of PID controller parameter tuning into the problem of open-close loop iterative learning control problem. The stability of PID controller parameter iterative learning control system and the astringency of controller parameters were verified for the first time through the construct of a compress mapping arithmetic operator, and a novel stop condition design scheme of integral type is advanced at the same time, the iterative learning of PID controller parameter was then successfully solved.

The ultimate simulation study validates the correctness and the effectiveness of the theory. Abstract: To maintain the outlet temperature at the desired set-point, a high-order outlet temperature control model is established for a water condenser, and different PID controller design methods are applied to its temperature control process.

The control function model is based on the analysis of the heat balance equation and the heat transfer rate equation. Abstract: In this work, PID design with acceptable performance and robustness of closed-loop system was introduced. With the normalized time constant of internal model control IMC replaced by a damping ratio and a new time constant, a modified IMC was proposed and could be equivalent to a proportional integral derivative PID control.

Since the control systems always have a dilemma between performance and robustness, the robust performance index was created with the integral of absolute error IAE weighted by the maximum sensitivity Ms with an exponential factor and the PID parameters were optimized through it. As an example, an empirical weighted factor 1. Simulation results show that the proposed PID control achieves good closed-loop performance and robustness. Authors: Aqeel S. Jaber, Abu Zaharin B. Ahmad, Ahmed N.

Many problems are subject to LFC such as a generating unit is suddenly disconnected by the protection equipment and suddenly large load is connected or disconnected. The frequency gets deviated from nominal value when the real power balance is harmed due to disturbances. LFC is responsible for load balancing and restoring the natural frequency to its natural position.

Pid Controller Design With Guaranteed Gain And Phase Margins

PSO optimization method is used to tuning the input and output gains for the fuzzy controller. The proposed method has been tested on two symmetrical thermal areas of an interconnected electrical power system. The simulation results are carried out in term of effectiveness of the frequency time response on its damping and compared it to common PID controller. The results show the performances of the proposed controller have quite promising compared to PID controller. Abstract: This paper describes the application of system identification techniques and robust control strategies to a pneumatic muscle actuator system.

Due to the inherent nonlinear and time-varying characteristics of this system, it is difficult to achieve excellent performance using conventional control methods. Therefore, we apply identification techniques to model the system as linear transfer functions and regard the un-modeled dynamics as system uncertainties. Because robust control is well-known for its capability in dealing with system uncertainties, we then apply robust control strategies to guarantee system stability and performance for the system.

This work is carried out in three parts. In this paper, we try to apply body's immune mechanism to the ABS-I position controller to overcome the weakness of traditional PID controller. In the synthesis, the immune PID controller with incomplete derivation can be obtained:. Therefore, the method for setting the parameters reasonably plays an important role in the improved PID controller with higher precision, faster response, and better robustness.

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The framework of GA-fuzzy-immune PID position controller with incomplete derivation can be built up as shown in Figure 4. According to the immune feedback mechanism of biological systems [ 42 ], four stages in the autoimmune reaction can be summarized as follows. In the initial stage, the antigen amount is higher and the antibody amount is expected to increase quickly, so the T s cell should be suppressed to produce. After a period of immunization, the restrained action on T s cell would decrease; in other words, the antibody should not increase continually.

When most of antigens have been eliminated, T s should increase quickly to restrain B cell and the production of antibody. Finally, when all of the antigens have been eliminated, both of antigen and antibody amount should keep stable till the immunization end. As a frequently used membership function, Gaussian membership function has the feature of good smoothness and can express the concept of fuzzy language more exactly; thus, it is applied for the proposed controller. Traditional genetic algorithm in solving the problem, especially the complex problems, is easily trapped in the local optimum and appeared premature convergence.

To settle this question, some improvements of traditional genetic algorithm are presented. The overall process can be described as follows. As a general coding method for GA, binary coding is used widely due to the simple processes of coding and decoding and easy operation of crossover and mutation. However, for a multivariable optimization problem, the string of binary gene is too long to result in lower search efficiency. In order to solve this problem, float-point genes are used in the optimization model. With this strategy, the number of variables is not limited; coding and decoding are not needed.

Furthermore, the precision and efficiency can be increased and the calculation speed is high. A mixed coding program is presented in the improved GA. During the initial stage, binary coding is adopted to quickly search for the area with excellent properties. In the later stage, float-point coding is used to improve the precision. By this generating method, the searching space is reduced and the operating rate is increased. In an evolution search process, an appropriate fitness function plays an important role in parameter optimization. In order to obtain satisfactory dynamic characteristics of the transition process, the integral of time multiplied absolute value of error ITAE is also provided as a comprehensive performance index, and the square of control input is introduced to prevent the control energy from growing too big.

The comprehensive performance index function [ 43 ] can be calculated as follows:. To avoid overshoot, the introduction of punitive function is essential in the function. For genetic algorithm, an individual is selected as a parent according to its fitness. In rank-based selection algorithm, all individuals of every generation are ranked in order of increasing fitness value. Because of its strong global search capability, crossover operator of GA can be regarded as the main operator, and due to its local search capability, mutation operator can be regarded as an auxiliary operator.

Self-adaptive crossover and mutation operators are proposed in this paper; in other words, crossover probabilities P c and mutation probabilities P m are automatically adjusted with the addition of evolutionary generations. In the initial stage, a larger P c and a smaller P m can effectively accelerate convergence velocity of iteration; however, in the later stage, a smaller P c and a larger P m would avoid local optimal solution. The formulas of P c and P m are given as follows:.


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To protect excellent individuals of each generation, the elitist strategy was applied in GA to improve the convergence and optimization results; thus, the best individual would be copied directly into next generation. In order to verify the performance of proposed GA-fuzzy-immune PID controller, a simulation example is provided in this section and the parameters are illustrated as follows.

The population size is set to 50, G m is set to , P c 1 is set to 0. The configurations of simulation environment for these controllers were uniform. The unit step responses of this system are shown in Figure 8. The first curve is response obtained with fuzzy inference, the second curve is response obtained with immune algorithm, the third curve is response obtained with fuzzy-immune inference F-I , the fourth curve is response obtained with real-coded GA, and the fifth curve is response obtained through integration of improved genetic algorithm and fuzzy-immune inference GA-F-I.

The PID parameters and performance indexes of the five control methods are shown in Table 2. The proposed controller parameters can be calculated by improved GA and fuzzy inference:. The settling time t s is reduced from 0. The rising time t r is reduced from 0. Although the rising time t r is not the best, the nonovershoot and shortest settling time can be achieved by the proposed PID controller. In this paper, a GA-fuzzy-immune PID controller was designed to improve the performance of robot dexterous hand.

In order to improve the characteristics of proposed controller, immune mechanism, genetic algorithm, and fuzzy inference were applied. Finally, a simulation experiment was carried out and the results showed that the designed controller was ideal. In future studies, the authors plan to investigate multifinger coordination control system. Furthermore, more intelligent control algorithms for multifinger coordination control system are worth further study for the authors. The authors declare that there is no conflict of interests regarding the publication of this paper.

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Journal List ScientificWorldJournal v. Published online Jul 6. Author information Article notes Copyright and License information Disclaimer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. Introduction In the past few years, massive research is committed to study the anthropomorphic robot hands with dexterous manipulation abilities.

Literature Review Recent publications relevant to this paper are mainly concerned with three research streams: robot dexterous hand control methods, PID control methods, and fuzzy-immunity feedback control methods. Robot Dexterous Hand Control Methods For the robot dexterous hand control methods, many researchers had worked on the problem and proposed different solutions since the last decades.

PID Control Methods As one of the earliest control strategies, PID control has been developed to deal with more complex control problems due to the advantages of simple description, high dependability, strong robustness, and so forth. Discussion However, although many approaches for robot dexterous hand have been proposed in above literatures, they have some common disadvantages summarized as follows.

Robot Dexterous Hand 3. Open in a separate window. Figure 1. The control circuit board of robot dexterous hand and the index finger.

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Analytical Design of PID Controllers | Ivan D Diaz-Rodriguez | Springer

Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Step 1 coding. Step 2 generating initial population. Step 3 selecting fitness function. Step 4 selection. Step 5 crossover and mutation. A Simulation Example In order to verify the performance of proposed GA-fuzzy-immune PID controller, a simulation example is provided in this section and the parameters are illustrated as follows.

Figure 8. Table 2 PID parameters and performance indexes of five control methods. Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. References 1. Bicchi A, Kumar V.

Robotic grasping and contact: a review. Mechanism and Machine Theory. Bio-inspired mechanical design of a tendon-driven dexterous prosthetic hand. A Mathematical Introduction to Robotic Manipulation. CRC Press; Yoshikawa T. Multifingered robot hands: Control for grasping and manipulation. Annual Reviews in Control. Tomovic R, Boni G.

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An adaptive artificial hand. A new variable structure PID-controller for robot manipulators with parameter perturbations: an augmented sliding surface approach. A new variable structure PID-controller design for robot manipulators. Atia KR. A new variable structure controller for robot manipulators with a nonlinear PID sliding surface. Adaptive fuzzy computed-torque control for robot manipulator with uncertain dynamics.

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Robust-tracking control for robot manipulator with deadzone and friction using backstepping and RFNN controller. Performance tuning for robot manipulators using intelligent robust controller. Robotics and Autonomous Systems. Han JQ. From PID to active disturbance rejection control. Semiglobal stability of saturated linear PID control for robot manipulators.

Synthesis of PID controllers for a class of time delay systems. Optimum setting for automatic controllers. ASME Transactions. Chen J, Huang T. Applying neural networks to on-line updated PID controllers for nonlinear process control. Journal of Process Control.

Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand

Pelusi D. Genetic-neuro-fuzzy controllers for second order control systems. On designing optimal control systems through genetic and neuro-fuzzy techniques. PID and intelligent controllers for optimal timing performances of industrial actuators. Fuzzy algorithm control effectiveness on drum boiler simulated dynamics.

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