OPTIMIZATION OF PID CONTROLLER FOR LIQUID LEVEL TANK SYSTEM USING ITs

OPTIMIZATION OF PID CONTROLLER FOR LIQUID LEVEL TANK SYSTEM USING INTLELLIGENT TECHNIQUES : 

OPTIMIZATION OF PID CONTROLLER FOR LIQUID LEVEL TANK SYSTEM USING INTLELLIGENT TECHNIQUES UNDER THE GUIDANCE OF: PRESENTED BY: Mr. ROHTASH DHIMAN BIJAY KUMAR (ASST. PROFESSOR) 09022228 DEPT. OF ELECT. ENGG. MTIC(2nd yr) EE DEPARTMENT DEENBANDHU CHHOTU RAM UNIVERSITY OF SCIENCE & TECHNOLOGY (MURTHAL) 27-Dec-11

Research Papers Out Of This Dissertation : 

Research Papers Out Of This Dissertation BIJAY KUMAR, ROHTASH DHIMAN “Optimization of PID Controller For Liquid Level Tank System Using Intelligent Techniques” in Canadian Journal on Electrical and Electronics Engineering (CJEEE) Canada. Vol.2, No.11, Nov 2011, Pages: 531-535, Paper ID: EEE- 1111-017, ISSN: 1923-0540. BIJAY KUMAR, ROHTASH DHIMAN “Tuning of PID controller for liquid level tank system using Intelligent Techniques" in International Journal of Computer Science and Technology (IJCST), India. IJCST Vol.2 Issue 4. Ver-2, OCT-DEC-2011, Pages:257-260, Paper ID: IJCST/622/4/C-112, ISSN: 0976-8491.

CONTENTS : 

CONTENTS Introduction Liquid Level Tank System PID Controller Ziegler–Nichols Tuning Method Genetic Algorithm Particle Swarm Optimization Comparative Results of Intelligent Techniques Conclusions & Future Work References

Introduction : 

Introduction Due to the requirement of industrial manufacturing processes, the liquid tank level control system is applied to many processing fields. The liquid level control system automatically maintains the desired level of water in a tank. The implementation of PID controllers needs proper tuning of proportional gains, integral gains, and derivative gains of the controllers. A conventional PID controller may have poor control performance, when it is used for controlling non-linear and complex processes. In this dissertation work, The GA and PSO programs are used to determine the optimal values of the PID controller parameters to improve the transient response of the system at time.

Liquid Level Tank System : 

Liquid Level Tank System Three-tank water level system is typical nonlinear time delay process control system. In this arrangement, there are three tanks: tank A, tank B, and tank C. Tank D is the main tank, which provides water for the pump. The control actuator is an electric valve. We will change the open range of the electric valve from 0% to 100%. Different open range of electric valve means the different water flow rate. Fig.1-Simple structure of water level system

Liquid Level Tank System : 

Liquid Level Tank System To optimize the parameters of PID controller Tank ‘C’ is analyzed. As we give delay of 0.5 in each tank than the system will come out to be third order model with time delay. Desired transfer function of three tank water level system is Fig.2-Water level system with control valve Fig.3-Water-Tank model appears in the simulation

PID Controller : 

PID Controller The PID controller is the most common form of feedback controllers. A PID controller calculates an "error" value as the difference between a desired set-point and a measured process variable. The controller attempts to minimize the error by adjusting the process control inputs. Fig.4-Block Diagram of PID controller

PID Controller : 

PID Controller PID stands for Proportional-Integral-Derivative. Proportional: Error multiplied by a gain, Kp. P Integral: The integral error is multiplied by a gain KI. Derivative: The rate of change of error multiplied by a gain, Kd.

Ziegler–Nichols Tuning Method : 

Ziegler–Nichols Tuning Method The Ziegler–Nichols tuning method is a heuristic method of tuning a PID controller. It is performed by setting the I (integral) and D (derivative) gains to zero. The "P" proportional gain, KP is then increased (from zero) until it reaches the ultimate gain KU, at which the output of the control loop oscillates with a constant amplitude. KU and the oscillation period PU are used to set the P, I, and D gains depending on the type of controller used: Table.1-Ziegler–Nichols Method

Cont… : 

Cont… KU = 0.052245. TU= 79.315004 Fig.5-Simulation block diagram of Controller and Plant Fig.6-Sustained oscillations appears in the simulation

P- Controller : 

P- Controller Fig.7-P Controller MATLAB/SIMULINK block diagram with error.

Result of P-Controller : 

Result of P-Controller Fig.8-Output response of P Controller Table.2- Steady State Response of P

PI-Controller : 

PI-Controller Fig.9-PI Controller MATLAB/SIMULINK block diagram with error.

Result of PI-Controller : 

Result of PI-Controller Fig.10-Output response of PI Controller Table.3- Steady State Response PI

PID-Controller : 

PID-Controller Fig.11-PID Controller MATLAB/SIMULINK block diagram with error.

Result of PID-Controller : 

Result of PID-Controller Fig.12-Output response of PID Controller Table.4- Steady State Response PID

Genetic Algorithms : 

Genetic Algorithms Genetic Algorithms (GAs) are a stochastic global search method that mimics the process of natural evolution. It is one of the methods used for optimization. There are four main stages of a genetic algorithm, these are known as Selection, Crossover, Mutation, Elitism,

Flow Chart of Genetic Algorithm : 

Flow Chart of Genetic Algorithm Create Population Measure Fitness Select Fitness Crossover Mutation Optimal Solution Non Optimal Solution Stopping Criterion Met NO YES

GA-PID : 

GA-PID Fig.13-GA-PID MATLAB/SIMULINK block diagram with error. Table.5- GA Parameter

Result of GA-PID : 

Result of GA-PID Fig.14-Output response of GA-PID Table.6- Steady State Response GA-PID

Particle Swarm Optimization : 

Particle Swarm Optimization PSO is a population based, stochastic search technique developed by Kennedy and Eberhart. The searching process of the algorithm was inspired by social behaviours of animals such as bird flocking and fish schooling. PSO starts with the random initialization of a population of individuals (particles) in the search space. PSO algorithm works on the social behaviour of particles in the swarm. Therefore, it finds the global best solution by simply adjusting the trajectory of each individual toward its own best location and toward the best particle of the entire swarm at each time step called generation.

Flow Chart of PSO : 

Flow Chart of PSO Initialize Particles with random Position and Velocity vectors For each Particle Position (p) evaluate the fitness If fitness (p) is greater than fitness of (pbest) than pbest=p Select best of pbest as gbest Update Position and Velocities of Particles Start End If gbest is the optimal YES NO

PSO-PID : 

PSO-PID Fig.15-PSO-PID MATLAB/SIMULINK block diagram with error. Table.7-PSO Parameter

Result of PSO-PID : 

Result of PSO-PID Fig.16-Output response of PSO-PID Table.8- Steady State Response PSO-PID

Comparative Results of Intelligent Techniques : 

Comparative Results of Intelligent Techniques Fig.17-Comparative Results of Intelligent Techniques

Cont.. : 

Cont.. Table.10-PID values of ZN, GA-PID, PSO-PID Table.9-Performance Indices of P, PI, PID, GA-PID and PSO-PID Controller

Conclusion : 

Conclusion Simulation result indicates that PSO-PID performance indices are also better than the GA-PID and Ziegler & Nichol method. ISE, IAE and ITSE value of PSO-PID is also less than the GA-PID and Ziegler & Nichol method. The gains obtained through may provide better responses than those obtained by the Ziegler-Nichols method and Genetic Algorithm method. In the future, the following work may be carried out. Hybrid Neuro Fuzzy (HNF) approach can be used to improve the performance of PSO based controller. Tabu Search (TS) algorithm can be used to optimize PSO based controller. In the present work, I have used simulation to show superiority of the algorithm. Further works may be done to apply it to real system.

References : 

References [1]. Mohammed Obaid Ali, S. P. Koh, K. H. Chong, S.K.Tiong and Zeyad Assi Obaid. “Genetic Algorithm Tuning Based PID Controller for Liquid-Level Tank System” Proceedings of the International Conference on Man-Machine Systems (ICoMMS) 11 – 13 October 2009, Batu Ferringhi, Penang, MALAYSIA. [2]. M. Willjuice Iruthayarajan, S. Baskar. “Optimization of PID parameters using Genetic algorithm and Particle swarm optimization”. IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007), Dr. M.G.R. University, Chennai, Tamil Nadu, India. Dec. 20-22, 2007. pp.81-86. [3]. Xiaoli Li, Ji Li and Longhui Shi. “Modelling and Simulation of Water Level System”. Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, China September 2008. [4]. Pan Aixian & Gao Yun. “Design and Application of Rough Controller in Three-tank System”. 2010 International Conference on Computing, Control and Industrial Engineering. [5]. Hiroe Fujiwara, Kouji Matsumoto, Hideyuki Nishida, Jun Ando, Masato Kawaura. “Development of PSO-based PID Tuning Method”. International Conference on Control, Automation and Systems 2008 Oct. 14-17, 2008 in COEX, Seoul, Korea. [6]. B.Nagaraj & Dr.N.Murugananth. “A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO”. 978-1-4244-7770-8/10/2010 IEEE. [7]. Wen-wen Cai , Li-xin Jia , Yan-bin Zhang , Nan Ni. “Design and simulation of intelligent PID controller based on particle swarm optimization”. 978-1-4244-7161-4/10/2010 IEEE.

References : 

References [8]. Guoming Huang, Dezhao Wu, Wailing Yang, Yuncan Xue. “Self-tuning of PID Parameters Based on the Modified Particle Swarm Optimization”. Proceedings of the 8th World Congress on Intelligent Control and Automation July 6-9 2010, Jinan, China. [9]. Maziyah Mat Noh, Muhammad Sharfi Najib, Nurhanim Saadah Abdullah. “Simulator of Water Tank Level Control System Using PID-Controller”. 3rd IASME / WSEAS Int. Conf. on WATER RESOURCES, HYDRAULICS & HYDROLOGY (WHH '08), University of Cambridge, UK, Feb. 23-25, 2008. [10]. T. K. Teng, J. S. Shieh and C. S. Chen. “Genetic algorithms applied in online auto-tuning PID parameters of a liquid-level control system”. Transactions of the Institute of Measurement and Control 2003; 25; 433. [11]. AYTEKIN BAGIS. “Determination of the PID Controller Parameters by Modified Genetic Algorithm for Improved Performance”. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 23, 1469-1480 (2007). [12]. Chengwei Li & Jiandong Lian. “The Application of Immune Genetic Algorithm in PID Parameter Optimization for Level Control System”. Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China. [13]. Chao Ou & Weixing, “Comparison between PSO and GA for Parameters Optimization of PID Controller”. Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation June 25 - 28, 2006, Luoyang, China .

References : 

References [14]. B. Nagaraj, S. Subha & B. Rampriya, “Tuning Algorithms for PID Controller Using Soft Computing Techniques”. IJCSNS International Journal of Computer Science and Network S 278 ecurity, VOL.8 No.4, April 2008. [15]. Mohammed El, Said El & Telbany, “Employing Particle Swarm Optimizer and Genetic Algorithms for Optimal Tuning of PID Controllers: A Comparative Study”. ICGST-ACSE Journal, Volume 7, Issue 2, November 2007. [16]. Miao Wang & Francesco Crusca. “Design and implementation of a gain scheduling controller for a water level control system”. ISA Transactions 41 ~2002! 323–331 [17]. Saifudin Bin Mohamed Ibrahim , “The PID Controller Design Using Genetic Algorithm ,” University of Southern Queensland Faculty of Engineering and Surveying, Bachelor of Engineering (Electrical and Electronics) Submitted: 27th October, 2005. [18]. S.M.Giriraj Kumar, Deepak Jayaraj & Anoop. R. Kishan. “PSO based tuning of a PID controller for a High performance drilling machine”. 2010 International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 19. [19]. David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. The University of Alabama, Addison-Wesley Publishing Company Inc, 1989. [20]. K Ogata, Modern Control Engineering, University of Minnesota, Prentice Hall, Fourth Edition1987.

References : 

References [21]. Khosro Khandani Ali Akbar & Jalali Mohammad Alipoor. “Particle Swarm Optimizer Based design of disturbance rejection PID controller for time delay system” 978-1-4244-4738-1/09/2009 IEEE. [22]. Yongwei Zhang, Fei Qiao, Jianfeng Lu, Lei Wang & Qidi Wu. “Performance Criteria Research on PSO-PID Control Systems”. 2010 International Conference on Intelligent Computing and Cognitive Informatics. 978-0-7695-4014-6/10/2010. [23]. Chengwei Li & Jiandong Lian, “The Application of Immune Genetic Algorithm in PID Parameter Optimization for Level Control System”. Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China

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