Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots

This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired veloc- ity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional- integral-derivative controllers, where neural networks and experts' knowledge are applied to tune parameters. Extensive experi- ments conducted on the simulated wheeled mobile robots show the effectiveness of this approach.

[1]  Marian Gheorghe,et al.  A Novel Membrane Algorithm Based on Particle Swarm Optimization for Solving Broadcasting Problems , 2012, J. Univers. Comput. Sci..

[2]  Peng-Yung Woo,et al.  Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators , 2006, IEEE Transactions on Industrial Electronics.

[3]  Malek Adjouadi,et al.  A generalized leaky Integrate-and-Fire Neuron Model with Fast Implementation Method , 2014, Int. J. Neural Syst..

[4]  Lu Ma,et al.  Synthetic Population Generation with Multilevel Controls: A Fitness‐Based Synthesis Approach and Validations , 2015, Comput. Aided Civ. Infrastructure Eng..

[5]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot: backstepping kinematics into dynamics , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[6]  Wu Wei.  GLOBAL TRAJECTORY TRACKING CONTROL OF MOBILE ROBOTS , 2001 .

[7]  Fumio Miyazaki,et al.  A stable tracking control method for an autonomous mobile robot , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[8]  Jianqiang Wang,et al.  Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control , 2015, Integr. Comput. Aided Eng..

[9]  Antoni Morro,et al.  Studying the Role of Synchronized and Chaotic Spiking Neural Ensembles in Neural Information Processing , 2014, Int. J. Neural Syst..

[10]  Sašo Blaič.  A novel trajectory-tracking control law for wheeled mobile robots , 2011 .

[11]  Christopher J. Rozell,et al.  Optimal Sparse Approximation with Integrate and Fire Neurons , 2014, Int. J. Neural Syst..

[12]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[13]  Bo Sun,et al.  Multiresolution streamline placement based on control grids , 2014, Integr. Comput. Aided Eng..

[14]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[15]  Ferrante Neri,et al.  An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems , 2014, Int. J. Neural Syst..

[16]  Jie Li,et al.  Evaluation and Application of a Hybrid Brain Computer Interface for Real Wheelchair Parallel Control with Multi-Degree of Freedom , 2014, Int. J. Neural Syst..

[17]  Mark Sumner,et al.  A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Jun Ye,et al.  Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot , 2008, Neurocomputing.

[19]  Cheng-Ching Yu,et al.  PID tuning rules for SOPDT systems: review and some new results. , 2004, ISA transactions.

[20]  Tzuu-Hseng S. Li,et al.  Design and implementation of an adaptive sliding-mode dynamic controller for wheeled mobile robots , 2009 .

[21]  M. Angels Colomer,et al.  Using a bioinspired model to determine the extinction risk of Calotriton asper populations as a result of an increase in extreme rainfall in a scenario of climatic change , 2014 .

[22]  Saso Blazic,et al.  A novel trajectory-tracking control law for wheeled mobile robots , 2011, Robotics Auton. Syst..

[23]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[24]  Catalin Buiu,et al.  Development of membrane controllers for mobile robots , 2012, Inf. Sci..

[25]  Salmiah Ahmad,et al.  Modelling and simulation of double-link scenario in a two-wheeled wheelchair , 2014, Integr. Comput. Aided Eng..

[26]  George K. I. Mann,et al.  Design and Tuning of Standard Additive Model Based Fuzzy PID Controllers for Multivariable Process Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Kyoung Kwan Ahn,et al.  Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network , 2006 .

[28]  Chun-Jung Chen,et al.  Motion control for a two-wheeled vehicle using a self-tuning PID controller , 2008 .

[29]  Hojjat Adeli,et al.  A neural dynamics model for structural optimization—Theory , 1995 .

[30]  Ferrante Neri,et al.  Optimization of Delayed-State Kalman-Filter-Based Algorithm via Differential Evolution for Sensorless Control of Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[31]  Juanjuan He,et al.  A hybrid membrane evolutionary algorithm for solving constrained optimization problems , 2014 .

[32]  Gheorghe Paun,et al.  The Oxford Handbook of Membrane Computing , 2010 .

[33]  Dongkyoung Chwa,et al.  Fuzzy Adaptive Tracking Control of Wheeled Mobile Robots With State-Dependent Kinematic and Dynamic Disturbances , 2012, IEEE Transactions on Fuzzy Systems.

[34]  Han-Xiong Li,et al.  A Saturation-Based Tuning Method for Fuzzy PID Controller , 2013, IEEE Transactions on Industrial Electronics.

[35]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[36]  Jin Bae Park,et al.  Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[37]  Shuli Sun.  Designing approach on trajectory-tracking control of mobile robot , 2005 .

[38]  Weiming Shen,et al.  An integrated feature-based dynamic control system for on-line machining, inspection and monitoring , 2015, Integr. Comput. Aided Eng..

[39]  Igor Skrjanc,et al.  Time optimal path planning considering acceleration limits , 2003, Robotics Auton. Syst..

[40]  Fereidoun Amini,et al.  A Wavelet‐Based Adaptive Pole Assignment Method for Structural Control , 2014, Comput. Aided Civ. Infrastructure Eng..

[41]  Hojjat Adeli,et al.  Optimization of space structures by neural dynamics , 1995, Neural Networks.

[42]  Tzuu-Hseng S. Li,et al.  EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots , 2009, Inf. Sci..

[43]  Gheorghe Paun,et al.  On the power of enzymatic numerical P systems , 2012, Acta Informatica.

[44]  Gerasimos G. Rigatos.  Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems , 2013, Integr. Comput. Aided Eng..

[45]  Ching-Hung Lee,et al.  Adaptive tracking control of nonholonomic mobile robots by computed torque , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[46]  Hojjat Adeli,et al.  Self-constructing wavelet neural network algorithm for nonlinear control of large structures , 2015, Eng. Appl. Artif. Intell..

[47]  Marian Gheorghe,et al.  Evolutionary membrane computing: A comprehensive survey and new results , 2014, Inf. Sci..

[48]  Eun-Kyung Kim,et al.  Adaptive PID Speed Control Design for Permanent Magnet Synchronous Motor Drives , 2015, IEEE Transactions on Power Electronics.

[49]  Qi Meng,et al.  A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems , 2013, Appl. Soft Comput..

[50]  Gexiang Zhang,et al.  A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning , 2015, Int. J. Comput. Commun. Control.

[51]  Catalin Buiu,et al.  Using enzymatic numerical P systems for modeling mobile robot controllers , 2011, Natural Computing.