I hereby certify that I am responsible for the work submitted in this project, that the original work is mine except as noted in the references and acknowledgments, and that the original work contained herein has not been taken or performed by unspecified sources or persons. This final year project is preceded by a topic titled “Optimization of PID Control Parameters Using Artificial Fish Shoal Algorithm”. The background of the topic is presented in the introductory chapter describing PID controllers.
Its simulation in MATLAB Simulink along with a block diagram is presented in the Results and Discussion sections. First and foremost, I would like to thank the associate professor Dr. To Irraivan Elamvazuthi, my supervisor, for his time, effort, guidance and honest encouragement. dr. Irraivan Elamvazuthi has been my inspiration in overcoming all obstacles to complete this work.
Special thanks to the final year project committee for their help, assistance and cooperation throughout the duration of the project. Last but not least, I would like to thank my family for all the support they give me.
INTRODUCTION
- PID Background
- Problem Statement
- Objective & Scope of Study
- Objective
- Scope of Study
- Project Feasibility
There are three separate parameters involved in the PID controller calculation;. 1) the proportional, 2) the integral and 3) the derived values. The current error is obtained by proportional value; the response based on the sum of the errors is determined by the Integral part, while the response on the rate at which the error has been changed is obtained by the Derivative part. In our study, we optimize PID controller parameters (Kp, Ki, Kd) using artificial swarming algorithm.
The purpose of this project is to perform optimization of PID controller parameters using AFSA to improve PID controller performance. This project is feasible in all respects as all adequate facilities are provided in terms of availability of laboratory equipment and software. Based on the proposed methodology architecture and Gantt chart, the project will be completed within the expected time frame.
LITERATURE REVIEW
- PID Tuning
- Swarm Techniques
- Swarm Intelligence
- Artificial Swarm Algorithm (ASFA)
- Searching Action
- Swarming Action
- Following Action
- Literature Regarding the Study
A new algorithm, AFSA, that is based on and follows the behavior of swarm techniques and artificial intelligence. In the shallow waters, the fish has the ability to find the area in the water that is more nutritious, and naturally a large number of fish gather in that part of the water. Therefore, an artificial fishing practice (AF) is proposed that mimics behaviors such as searching, swarming and tracking [6].
In water, fish have the natural ability to find the area that is most abundant with food. In the aggregation process, fish try to avoid jamming with other species and move in the same direction as other fish [6]. The objective of AMF is food consistency in water and all of the above activities can require solution space [6].
According to Table 1, the literature shows that the swarm optimization has been used in various applications such as in neural networks, wireless sensor network deployment, and various control applications. In this study, the objective is to optimize the PID controller parameters using artificial swarming algorithm.
METHODOLOGY
- PID Tuning using Zeigler-Nichols Method
- Ziegler-Nichols Closed Loop Tuning Method
- The Artificial Fish Swarm Algorithm
- The Structure of Algorithm
- Behavior Description
- Behavior Selection
- Fitness Function Design
- Feasible Region of PID Parameter Design
- Termination Condition
- Flow of Algorithm
- Working with new Parameters
- PID Pressure Control
A behavior based on the artificial fish is created and the model summarizes the behavior of the artificial fish. FC=f(X), represents the current position of food consistency AF where the objective function is FC. The AF model has assumed the most importance in the overall process of PID Parameter Optimization using AMF.
In the study, the parameters to be optimized are Kp, Ki and Kd. The Algorithm sets a bulletin board and its purpose is to store the optimal position of AF and food consistency the AF at corresponding position. The AF environment is evaluated based on the characteristics of the problem needed to be solved and selection of action.
Derive the values using swarming and tracking behavior and the one with the maximum results would be the choice to implement. So for better convergence, a larger view would be preferable, while the larger step is good for convergence. If the number of AF is high, the size of the local extremum would be large, leading to a higher convergence rate, but the number of iterations will be larger.
ASFA creates a group of parameter initializations such as the fish colony to narrow the range of PID parameters being optimized. In AFSA, the convergence is considered and to terminate the algorithm when there is no higher value of the fitness function after a number of iterations of search. Step 1: initialize the number of fish, the visibility range, δ and enter a random position of AF, X=(x1,x2,...,xn).
Initially the parameters set by Ziegler-Nichols method (Kp*, Ki*, Kd*) as in Figure 4. The new parameters found from AFSA denoted as (Kp, Ki, Kd) would be used as in Figure 5. For the experimental purpose, Flow controller PILOT Plant is used as process/plant in our study.
RESULTS & DISCUSSIONS
PID Pressure Control
- Closed Loop Tuning of Secondary Loop
- Optimization of PID Parameters using AFSA
- Validation of Parameters on PID Pressure Control Pilot Plant
- Observation & Discussion
The closed-loop Ziegler-Nichols tuning method is used to set the parameters of the pressure device PID controller, the parameters are shown in Table 4. At first, the PID controller is tuned by Ziegler-Nichol PID tuning, and the results are shown in Table 4. Then the PID parameters are tuned according to an intelligent method and the device is simulated using an artificial algorithm.
Regarding Figure 13, the new optimized parameters using the corresponding method are shown in Table 5. For the validation of the study in the real-world plant, the parameters are applied to see the controller response. Since the response in Figure 15 using AMF does not reach the set point because the SIM305-GAMN-FF-BATCH PID Pilot Pressure Control Plant allows only one digit after the decimal point.
Ki=0.0022 is almost equal to zero, since the integral part of the PID controller improves the compensation of the system [3]. Therefore, the controller parameters must be fine-tuned to achieve maximum performance. Respecting the optimization of the PID controller parameters of the PID pressure control plant, from Table 5 AMF shows very good performance.
The study is compared with other Optimization method to validate and observe the mutual response of different optimization approaches. In relation to Figure 16, the comparison of the parameters and the percentage over the throughput of the respective optimization approaches are illustrated in Table 6. Observing the optimization of the PID parameters using different approaches, Table 6 shows that the intelligent methods present very acceptable results over the conventional methods of PID. Tuning.
This study shows that the optimization of PID parameters using the artificial swarm algorithm gives very accurate results when compared to Ziegler-Nichols optimization and to particle swarm optimization. While validating the new parameters generated using AFSA in the PID pressure control plant, the output change Δ does not reach the set point because the front panel of the PID controller only accepts one digit after the decimal point as shown in the figure. 17. Since the integrator component of the PID controller is Ki=0.002, the controller assumes zero, the controller works as a PD controller.
Because the Integrator component removes the offset and allows the controller to reach the setpoint, AFSA's response only reaches the setpoint. Therefore, the controller parameters must be fine-tuned to achieve maximum performance.
Other Examples of Optimization Using AFSA
PID parameters of all plants are tuned using Artificial Fish Swarm Algorithm, Particle Swarm Algorithm and Conventional Ziegler-Nichols. Plants being tuned using AMF have relatively lower overshoots than other approaches and setup time is also reduced compared to other methods.
CONCLUSION
Li, Optimization of PID Controllers Using Artificial Fish Swarm Algorithm, Proceedings of International Conference on Automation and Logistics, August Jinan, China. Wang, Optimization of PID controller parameters based on an improved artificial fish swarm algorithm, Proceedings of Third International Workshop on Advanced Computational Intelligence, August Suzhou, Jiangsu, China.
