Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

This paper deals with design parameter selection of a discrete-time neural second order sliding mode controller for unknown nonlinear systems, based on bacterial foraging optimization. First, a neural identifier is proposed in order to obtain a mathematical model for the unknown discrete-time nonlinear systems, then a novel second order sliding mode controller is proposed. Finally, both, the neural identifier and the controller are optimized using bacterial foraging algorithm. In order to illustrate the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator.

[1]  Ridha Ben Abdennour,et al.  Real-time application of discrete second order sliding mode control to a chemical reactor , 2009 .

[2]  Sharad Singhal,et al.  Training Multilayer Perceptrons with the Extende Kalman Algorithm , 1988, NIPS.

[3]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[4]  Ajith Abraham,et al.  Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis , 2009, IEEE Transactions on Evolutionary Computation.

[5]  Oscar Castillo,et al.  Genetic Optimization of Membership Functions in Modular Fuzzy Controllers for Complex Problems , 2013, Recent Advances on Hybrid Intelligent Systems.

[6]  Madhusudan Singh,et al.  Adaptive Control of Nonlinear Systems Using Bacterial Foraging Algorithm , 2011 .

[7]  Oscar Castillo,et al.  Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm , 2013, Expert Syst. Appl..

[8]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[9]  J. Grizzle,et al.  The Extended Kalman Filter as a Local Asymptotic Observer for Nonlinear Discrete-Time Systems , 1992, 1992 American Control Conference.

[10]  Amit Konar,et al.  Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain , 1999 .

[11]  Manolis A. Christodoulou,et al.  Adaptive Control with Recurrent High-order Neural Networks , 2000, Advances in Industrial Control.

[12]  Aleksej F. Filippov,et al.  Differential Equations with Discontinuous Righthand Sides , 1988, Mathematics and Its Applications.

[13]  Quanmin Zhu,et al.  Stable adaptive neurocontrol for nonlinear discrete-time systems , 2004, IEEE Trans. Neural Networks.

[14]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[15]  Alma Y. Alanis,et al.  Neural-PSO second order sliding mode controller for unknown discrete-time nonlinear systems , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[16]  Patricia Melin,et al.  Modular Neural Networks Architecture Optimization with a New Evolutionary Method Using a Fuzzy Combination Particle Swarm Optimization and Genetic Algorithms , 2009, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition.

[17]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[18]  Oscar Castillo,et al.  Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory , 2002, IEEE Trans. Neural Networks.

[19]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[20]  Alma Y. Alanis,et al.  Second order sliding mode for MIMO nonlinear uncertain systems based on a neural identifier , 2010, 2010 World Automation Congress.

[21]  Ridha Ben Abdennour,et al.  An asymptotic discrete second order sliding mode control law for highly non stationary systems , 2008, 2008 IEEE International Conference on Emerging Technologies and Factory Automation.

[22]  Jaime A. Moreno,et al.  A Lyapunov approach to second-order sliding mode controllers and observers , 2008, 2008 47th IEEE Conference on Decision and Control.

[23]  Amit Konar,et al.  On Stability of the Chemotactic Dynamics in Bacterial-Foraging Optimization Algorithm , 2009, IEEE Trans. Syst. Man Cybern. Part A.

[24]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[25]  Jin Zhang,et al.  Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Livjeet Kaur,et al.  Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm , 2012 .

[27]  M. O. Tokhi,et al.  Control of a single-link flexible manipulator using improved bacterial foraging algorithm , 2010, 2010 IEEE Conference on Open Systems (ICOS 2010).

[28]  A. Levant.  Sliding order and sliding accuracy in sliding mode control , 1993 .

[29]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[30]  Oscar Castillo,et al.  Statistical comparison of type-1 and type-2 fuzzy systems design with genetic algorithms in the case of three tank water control , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[31]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .