Design and experiments for model-free PI control of DC drives

This paper proposes the model-free PI control design for direct current (DC) drives. A model-free PI control system structure is considered in the framework of reference tracking control using a first-order nonlinear dynamic system as a local approximation of the process model. The derivatives are estimated numerically using a Savitzky-Golay filter that solves the differentiation and also the smoothing. The model-free PI control system structure is applied to the speed control of a laboratory nonlinear DC drive as a representative mechatronics application. The experimental results for three different reference input shapes show the very good performance in model reference tracking, reference trajectory tracking and numerical differentiation.

[1]  Plamen P. Angelov,et al.  On-line Design of Takagi-Sugeno Models , 2003, IFSA.

[2]  Michel Fliess,et al.  Model-Free Based Water Level Control for Hydroelectric Power Plants , 2010 .

[3]  S. Preitl,et al.  Stable Iterative Correlation-based Tuning algorithm for servo systems , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[4]  Cédric Join,et al.  Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control? , 2009, ArXiv.

[5]  R. Precup,et al.  Stability analysis method for fuzzy control systems dedicated controlling nonlinear processes , 2007 .

[6]  Florin Gheorghe Filip,et al.  Simulation-based Optimization using Genetic Algorithms for Multi-objective Flexible JSSP , 2011 .

[7]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.

[8]  Brigitte d'Andréa-Novel,et al.  Model-free control of automotive engine and brake for Stop-and-Go scenarios , 2009, 2009 European Control Conference (ECC).

[9]  BRATISLAV DANKOVIĆ,et al.  A Class of Almost Orthogonal Filters , 2009, J. Circuits Syst. Comput..

[10]  Bin Jiao,et al.  A Cooperative Co-evolutionary Quantum Particle Swarm Optimizer based on Simulated Annealing for Job Shop Scheduling Problem , 2011 .

[11]  Carlos Balaguer,et al.  A Model-Free Approach for Accurate Joint Motion Control in Humanoid Locomotion , 2011, Int. J. Humanoid Robotics.

[12]  Quanyan Zhu,et al.  Robust and resilient control design for cyber-physical systems with an application to power systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[13]  Cédric Join,et al.  Vers une commande multivariable sans modèle , 2006, ArXiv.

[14]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[15]  Z. Hou,et al.  On Data-driven Control Theory: the State of the Art and Perspective: On Data-driven Control Theory: the State of the Art and Perspective , 2009 .

[16]  Cédric Join,et al.  Model-Free Control of Shape Memory Alloys Antagonistic Actuators , 2008 .

[17]  Stefan Preitl,et al.  Fuzzy controllers for tire slip control in anti-lock braking systems , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[18]  Michel Fliess,et al.  Model-free control of dc/dc converters , 2010, 2010 IEEE 12th Workshop on Control and Modeling for Power Electronics (COMPEL).

[19]  William H. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[20]  J Richalet,et al.  An approach to predictive control of multivariable time-delayed plant: stability and design issues. , 2004, ISA transactions.

[21]  Kevin Kok Wai Wong,et al.  Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[22]  You Zhou,et al.  GPU-Based Parallel Multi-objective Particle Swarm Optimization , 2011 .

[23]  J. Vascak,et al.  Path planning in dynamic environment using Fuzzy Cognitive Maps , 2008, 2008 6th International Symposium on Applied Machine Intelligence and Informatics.

[24]  Hou Zhong,et al.  On Data-driven Control Theory:the State of the Art and Perspective , 2009 .

[25]  M .,et al.  Some hybrid models to improve Firefly algorithm performance , 2011 .

[26]  Gerhard P. Hancke,et al.  Intelligent computing for the management of changes in industrial engineering modeling processes , 2005, IEEE 3rd International Conference on Computational Cybernetics, 2005. ICCC 2005..

[27]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[28]  Horia-Nicolai Teodorescu Pattern Formation and Stability Issues in Coupled Fuzzy Map Lattices , 2011 .

[29]  Cédric Join,et al.  Numerical differentiation with annihilators in noisy environment , 2009, Numerical Algorithms.

[30]  P. Baranyi,et al.  Definition and synergies of cognitive infocommunications , 2012 .

[31]  Cédric Join,et al.  Non-linear estimation is easy , 2007, Int. J. Model. Identif. Control..

[32]  Peter J. Fleming,et al.  Nonlinear identification of aircraft gas-turbine dynamics , 2003, Neurocomputing.

[33]  M. Manic,et al.  Computational intelligence based anomaly detection for Building Energy Management Systems , 2012, 2012 5th International Symposium on Resilient Control Systems.

[34]  Jean Sallantin,et al.  A dual model-free control of underactuated mechanical systems, application to the inertia wheel inverted pendulum , 2012, 2012 American Control Conference (ACC).

[35]  Levente Kovács,et al.  Robust servo control of a novel type 1 diabetic model , 2011 .

[36]  Cédric Join,et al.  Revisiting some practical issues in the implementation of model-free control , 2011 .

[37]  Inés Tejado,et al.  Data-driven fractional PID control: application to DC motors in flexible joints , 2012 .

[38]  Peter Baranyi,et al.  A unified terminology for the structure and semantics of CogInfoCom channels , 2012 .