Effect of Adaptation Gain in Model Reference Adaptive Controlled Second Order System

Adaptive control involves modifying the control law used by the controller to cope with the fact that the parameters of the system being controlled change drastically due to change in environmental conditions or in system itself. This technique is based on the fundamental characteristic of adaptation of living organism. The adaptive control process is one that continuously and automatically measures the dynamic behavior of plant, compares it with the desired output and uses the difference to vary adjustable system parameters or to generate an actuating signal in such a way so that optimal performance can be maintained regardless of system changes. Nature of adaptation mechanism for controlling the system performance is greatly affected by the value of adaptation gain. It is observed that for the lower order system wide range of adaptation gain can be used to study the performance of the system. As the order of the system increases the applicable range of adaptation gain becomes narrow. This paper deals with application of model reference adaptive control scheme to second order system with different values of adaptation gain. The rule which is used for this application is MIT rule. Simulation is done in MATLAB and simulink and the results are compared for varying adaptation mechanism due to variation in adaptation gain.

[1]  Peng Jia,et al.  ANN-based PID controller for an electro-hydraulic servo system , 2008, 2008 IEEE International Conference on Automation and Logistics.

[2]  M.S. Ehsani Adaptive Control of Servo Motor by MRAC Method , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[3]  Guang Ren,et al.  Design and Stability Analysis of Fuzzy Switching PID Controller , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[4]  Kuo-Ming Chang Model reference adaptive control for uncertain systems with sector-like bounded nonlinear inputs , 2005, 2005 International Conference on Control and Automation.

[5]  D. Simon Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[6]  Yorgo Istefanopulos,et al.  A new variable structure PID-controller design for robot manipulators , 2005, IEEE Transactions on Control Systems Technology.

[7]  Mahmoud Oukati Sadegh,et al.  A systematic method for the design of a full scale fuzzy PID controller for SVC to control power system stability , 2003 .

[8]  A. Rubaai,et al.  DSP-Based Implementation of Fuzzy-PID Controller Using Genetic Optimization for High Performance Motor Drives , 2007, 2007 IEEE Industry Applications Annual Meeting.

[9]  Rey-Chue Hwang,et al.  A new fuzzy PID-like controller , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[10]  Rey-Chue Hwang,et al.  The model reference control by auto-tuning PID-like fuzzy controller , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[11]  K. Benjelloun,et al.  A modified model reference adaptive control algorithm for DC servomotor , 1993, Proceedings of IEEE International Conference on Control and Applications.

[12]  Maurizio Cirrincione,et al.  An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model , 2005, IEEE Transactions on Industrial Electronics.

[13]  Tae-Yong Choi,et al.  The hybrid SOF-PID controller for a MIMO nonlinear system , 2005, Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics..

[14]  Dan Sun,et al.  A Single Neuron PID Controller Based PMSM DTC Drive System Fed by Fault Tolerant 4-Switch 3-Phase Inverter , 2006, 2006 1ST IEEE Conference on Industrial Electronics and Applications.

[15]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[16]  M.E.H. Benbouzid,et al.  Fuzzy Model Reference Adaptive Control of power converter for unity power factor and harmonics minimization , 2007, 2007 International Conference on Electrical Machines and Systems (ICEMS).

[17]  Ming-Ji Yang,et al.  Model Reference Adaptive Control Design for a Shunt Active Power Filter System , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[18]  Jie Wu,et al.  Lyapunov's stability theory-based model reference adaptive control for permanent magnet linear motor drives , 2004, Proceedings. 2004 First International Conference on Power Electronics Systems and Applications, 2004..

[19]  Rajesh Kumar Nema,et al.  Effect of Adaptation Gain on system Performance for Model Reference Adaptive Control Scheme using MIT Rule , 2010 .

[20]  T. John Koo,et al.  Stable model reference adaptive fuzzy control of a class of nonlinear systems , 2001, IEEE Trans. Fuzzy Syst..

[21]  A. Rubaai,et al.  DSP-Based Laboratory Implementation of Hybrid Fuzzy-PID Controller Using Genetic Optimization for High-Performance Motor Drives , 2008, IEEE Transactions on Industry Applications.

[22]  Kim-Fung Man,et al.  An optimal fuzzy PID controller , 2001, IEEE Trans. Ind. Electron..

[23]  Gang Liu,et al.  Adaptive Neuro-Fuzzy Inference System PID controller for SG water level of nuclear power plant , 2009, 2009 International Conference on Machine Learning and Cybernetics.