Discrete-time adaptive backstepping control: Application to pumping station

This article proposes an application of a discrete-time adaptive backstepping control strategy for a hydraulic process pumping station. The proposed solution leads to improved control system performances in terms of pressure and flow tracking in transient and standstill operation and improvement of pressure response time. The proposed design methodology is based on accurate model for pumping station, which is developed in previous works using fuzzy-C means algorithm. The control law design is based on discrete-time adaptive backstepping control, which is developed in the sense of Lyapunov stability theory using sign function, in order to satisfy various objectives of a stable pumping station like the asymptotic stability of the tracking error. To validate the proposed solution, simulation and experimental tests are made and analyzed. Compared to the conventional proportional–integral approach, the results show that the discrete-time adaptive backstepping control allows exhibiting excellent transient response over a wide range of operating conditions and especially is easier to be implemented in practice.

[1]  G. Roux,et al.  Fuzzy Optimal Control Design for Discrete Affine Takagi-Sugeno Fuzzy Models: Application to a Biotechnological Process , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[2]  Hai-Peng Ren,et al.  Adaptive control of hydraulic position servo system using output feedback , 2017, J. Syst. Control. Eng..

[3]  Onder Tutsoy,et al.  Adaptive estimator design for unstable output error systems: A test problem and traditional system identification based analysis , 2015, J. Syst. Control. Eng..

[4]  Abdelkader Chaari,et al.  A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization , 2012, Int. J. Appl. Math. Comput. Sci..

[5]  Stefano Marsili-Libelli,et al.  A Fuzzy Decision Support System for irrigation and water conservation in agriculture , 2015, Environ. Model. Softw..

[6]  Abderrahmen Zaafouri,et al.  Modeling, Identification and Control of Irrigation Station with Sprinkling: Takagi-Sugeno Approach , 2015, Complex System Modelling and Control Through Intelligent Soft Computations.

[7]  Zhengqiang Zhang,et al.  Adaptive backstepping control that is equivalent to tuning functions design , 2016 .

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  P. Kokotovic,et al.  Nonlinear control via approximate input-output linearization: the ball and beam example , 1992 .

[10]  A. Morse,et al.  Adaptive control of single-input, single-output linear systems , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[11]  Zengqiang Mi,et al.  Adaptive Robust Backstepping Control of Permanent Magnet Synchronous Motor Chaotic System with Fully Unknown Parameters and External Disturbances , 2016 .

[12]  Sundarapandian Vaidyanathan,et al.  Takagi-Sugeno fuzzy logic controller for Liu-Chen four-scroll chaotic system , 2016, Int. J. Intell. Eng. Informatics.

[13]  Xavier Guillaud,et al.  On the Backstepping Approach for VSC-HVDC and VSC-MTDC Transmission Systems , 2017 .

[14]  Shaocheng Tong,et al.  Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone , 2016, IEEE Transactions on Fuzzy Systems.

[15]  Jianbin Qiu,et al.  A Combined Fault-Tolerant and Predictive Control for Network-Based Industrial Processes , 2016, IEEE Transactions on Industrial Electronics.

[16]  Vinay Kumar Deolia,et al.  Backstepping Control of Discrete-Time Nonlinear System Under Unknown Dead-zone Constraint , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[17]  Jianbin Qiu,et al.  Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems , 2016, IEEE Transactions on Fuzzy Systems.

[18]  Chiheb Ben Regaya,et al.  An improved Fuzzy Logic control of irrigation station , 2017, 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT).

[19]  A. Zaafouri,et al.  An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process , 2018, Trans. Inst. Meas. Control.

[20]  Young Hoon Joo,et al.  Local stability and local stabilization of discrete-time T–S fuzzy systems with time-delay , 2016 .

[21]  Woo-seok Jang,et al.  Optimized fuzzy clustering by predator prey particle swarm optimization , 2007, IEEE Congress on Evolutionary Computation.

[22]  Gholam Rreza Rokni Lamooki,et al.  Recursive partial stabilization: Backstepping and generalized strict feedback form , 2013 .

[23]  I. Kanellakopoulos,et al.  Systematic Design of Adaptive Controllers for Feedback Linearizable Systems , 1991, 1991 American Control Conference.

[24]  R. Shantha Selva Kumari,et al.  Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images , 2016, Journal of Medical Systems.

[25]  Jianbin Qiu,et al.  Adaptive Fuzzy Backstepping Control for A Class of Nonlinear Systems With Sampled and Delayed Measurements , 2015, IEEE Transactions on Fuzzy Systems.

[26]  Jianbin Qiu,et al.  Adaptive Neural Control of Stochastic Nonlinear Time-Delay Systems With Multiple Constraints , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[27]  Noureddine Zahid,et al.  Fuzzy clustering based on K-nearest-neighbours rule , 2001, Fuzzy Sets Syst..

[28]  P. Kokotovic,et al.  A positive real condition for global stabilization of nonlinear systems , 1989 .

[29]  Chia-Feng Juang,et al.  Hierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[30]  Faouzi M'sahli,et al.  Model-based Predictive and Backstepping controllers for a state coupled four-tank system with bounded control inputs: A comparative study , 2015, J. Frankl. Inst..

[31]  M. Boussak,et al.  Sensorless Indirect Stator Field Orientation Speed Control for Single-Phase Induction Motor Drive , 2009, IEEE Transactions on Power Electronics.

[32]  Mihailo R. Jovanovic,et al.  Architecture Induced by Distributed Backstepping Design , 2007, IEEE Transactions on Automatic Control.

[33]  Jianbin Qiu,et al.  Network-Based Fuzzy Control for Nonlinear Industrial Processes With Predictive Compensation Strategy , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[34]  Chiheb Ben Regaya,et al.  DSP-based adaptive backstepping using the tracking errors for high-performance sensorless speed control of induction motor drive. , 2016, ISA transactions.

[35]  Abderrahmen Zaafouri,et al.  Hybrid control of a station of irrigation by sprinkling: Fuzzy supervisor approach , 2015, 2015 4th International Conference on Systems and Control (ICSC).

[36]  Onder Tutsoy,et al.  Design and Comparison Base Analysis of Adaptive Estimator for Completely Unknown Linear Systems in the Presence of OE Noise and Constant Input Time Delay , 2016 .

[37]  Jianbin Qiu,et al.  Distributed Fuzzy $H_{\infty }$ Filtering for Nonlinear Multirate Networked Double-Layer Industrial Processes , 2017, IEEE Transactions on Industrial Electronics.

[38]  Ricardo J. G. B. Campello,et al.  Takagi–Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions , 2013, IEEE Transactions on Cybernetics.

[39]  Indra Narayan Kar,et al.  Robust control of nonholonomic wheeled mobile robot with past information: Theory and experiment , 2017, J. Syst. Control. Eng..

[40]  Ayman El-Badawy,et al.  A novel disturbance observer-based backstepping controller with command filtered compensation for a MIMO system , 2016, J. Frankl. Inst..

[41]  Nelson Wiley Passivity and Global Stabilization of Cascaded Nonlinear Systems , 1992 .

[42]  Wei Xing Zheng,et al.  Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory. , 2017, IEEE transactions on cybernetics.

[43]  Feng Xia,et al.  Control and Scheduling Codesign: Flexible Resource Management in Real-Time Control Systems , 2008 .

[44]  Tian Yu,et al.  Parametric adaptive estimation and backstepping control of electro-hydraulic actuator with decayed memory filter. , 2016, ISA transactions.

[45]  Abderrahmen Zaafouri,et al.  Control and Modelling Using Takagi-Sugeno Fuzzy Logic of Irrigation Station by Sprinkling , 2014 .