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An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

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Języki publikacji
EN
Abstrakty
EN
This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
Rocznik
Strony
345--365
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
autor
  • Department of Instrumentation and Control Systems Engineering, PSG College of Technology Coimbatore, India-641004
autor
  • Department of Instrumentation and Control Systems Engineering, PSG College of Technology Coimbatore, India-641004
Bibliografia
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  • [2] Tao C.W., Taur J.S., Hsieh T.W., Tsai C.L., Design of a fuzzy controller with fuzzy swing-up and parallel distributed pole assignment schemes for an inverted pendulum and cart system. IEEE Transactions on Control Systems Technology 16(6): 1277-1288 (2008).
  • [3] Jia-Jun Wang, Simulation studies of inverted pendulum based on PID controllers, Simulation Modelling Practice and Theory 19(2): 440-449 (2011).
  • [4] Nenad Muskinja, Boris Tovornik, Swinging Up and Stabilization of a Real Inverted Pendulum, IEEE Transactions on Industrial Electronics 53(2): 631-639 (2006).
  • [5] Wai R.J., Chang L.J., Adaptive stabilizing and tracking control for a nonlinear inverted-pendulum system via sliding-mode technique. IEEE Transactions on Industrial Electronics 53(2): 674-692 (2007).
  • [6] Chang L.H., Lee A.C., Design of nonlinear controller for bi-axial inverted pendulum system. IET Control Theory and Application 1(4): 979-986 (2007).
  • [7] Shahnazi R., Akbarzadeh T.M.R., PI adaptive fuzzy control with large and fast disturbance rejection for a class of uncertain nonlinear systems, IEEE Transactions on Fuzzy Systems 16(1), 187-197 (2008).
  • [8] Solihin M.I., Akmeliawati R., Particle SwamOptimization for Stabilizing Controller of a Self-erecting Linear Inverted Pendulum. International Journal of Electrical and Electronic Systems Research 3: 410-415 (2010).
  • [9] Saifizul A.A., Zainon A N.A.B., Osman N.A.B. et al., Intelligent Control for Self-erecting Inverted Pendulum Via Adaptive Neuro-fuzzy Inference System. American Journal of Applied Sciences 3(4): 1795-1802 (2006).
  • [10] Xueming Yang, Jinsha Yuan, Jiangye Yuan, Huina Mao, A modified particle swarm optimizer with dynamic adaptation. Applied Mathematics and Computation 189(2): 1205-1213 (2007).
  • [11] Amin Hasanzadeha, Chris S. Edrington, Hossein Mokhtari, Optimal tuning of linear controllers for power electronics/power systems applications. Electric Power Systems Research 81: 2188-2197 (2011).
  • [12] Aamir H.O.A, Martino O.A., Matthew W.D., New Approach for Position Control of Induction motor. 45th Universities Power Engineering Conference (2010).
  • [13] Desineni S.N., Optimal Control Systems. CRC press (2003).
  • [14] Robandia I., Nishimori K., Nishimura R., Ishihara N., Optimal feedback control design using genetic algorithm in multimachine power system. Electrical Power and Energy Systems 23: 263-271 (2001).
  • [15] Li Jimin, Shang Chaoxuan, Zou Minghu, Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm. TSINGHUA Science and Technology 12(51): 208-211 (2007).
  • [16] Rahul Malhotra, Narinder Singh, Yaduvir Singh, Genetic Algorithms: Concepts, Design for Optimization of Process Controllers. Computer and Information Science 4(2): 39-54 (2011).
  • [17] Kennedy J., Eberhart R., Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN) 4: 1942-1948 (1995).
  • [18] Eberhart R., Kennedy J., A new optimizer using particle swarm theory. Proceedings of 6th International Symposium on Micro Machine and Human Science (MHS), pp. 39-43 (1995).
  • [19] Venayagamoorthy G.K., Harley R.G., Swarm Intelligence for Transmission System Control. Proceedings of the IEEE Conference on Power Engineering (2007).
  • [20] Eberhart R., Shi Y., Kennedy J., Swarm intelligence. San Francisco, CA: Morgan Kaufmann, (2001).
  • [21] Che-Cheng Chang, Jichiang Tsai and Shi-Jia Pei, A Quantum PSO Algorithm for Feedback Control of Semi-Autonomous Driver Assistance Systems. 12th International Conference on ITS Telecommunications (2012).
  • [22] Engelbrecht A.P., Particle swarm optimization: where does it belong? Proc. of the IEEE Swarm Intelligence Symposium (SIS'06): 48-54 (2006).
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  • [25] Ahmad Nickabadi, Mohammad Mehdi Ebadzadeh, Reza Safabakhsh, A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11: 3658-3670 (2011).
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  • [27] Sidhartha Panda, Narayana Prasad Padhy, Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied Soft Computing 8: 1418-1427 (2008).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-f43210f5-fb5c-4ddc-9be2-96b3add895c5
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