Modeling and Control Approach to Coupled Tanks Liquid Level System Based on Function-Type Weight RBF-ARX Model

A multi-input multi-output MIMO FWRBF-ARX model, which adopts radial basis function RBF neural networks with function-type weights FWRBF to approximate the coefficients of the state-dependent AutoRegressive model with eXogenous input variables SD-ARX, is utilized for describing the dynamics of a coupled tanks liquid system. Based on local linearization information of the MIMO FWRBF-ARX model, a predictive control strategy is proposed. In the algorithm, the control actions of the model predictive control MPC are calculated based on the local linearization of the MIMO FWRBF-ARX model at current working point. Real-time control experiments are carried out on the coupled tanks liquid system. The detailed comparative experiments demonstrate the feasibility and effectiveness of the proposed modeling and model-based control strategy for the coupled tanks plant.

[1]  Zheng Yan,et al.  Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Behrooz Safarinejadian,et al.  Parallel distributed compensator design of tank level control based on fuzzy Takagi-Sugeno model , 2014, Appl. Soft Comput..

[3]  Yukihiro Toyoda,et al.  A parameter optimization method for radial basis function type models , 2003, IEEE Trans. Neural Networks.

[4]  Jun Wu,et al.  Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model , 2015, J. Frankl. Inst..

[5]  Min Gan,et al.  A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling , 2010, Inf. Sci..

[6]  Maciej Ławryńczuk,et al.  Explicit nonlinear predictive control algorithms with neural approximation , 2014 .

[7]  Subhabrata Ray,et al.  Sliding mode control of quadruple tank process , 2009 .

[8]  Jun Wu,et al.  RBF-ARX model-based MPC strategies with application to a water tank system , 2015 .

[9]  Jairo Espinosa,et al.  A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark , 2011 .

[10]  C. L. Philip Chen,et al.  Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series , 2016, Int. J. Syst. Sci..

[11]  Daisuke Ikeda,et al.  Design of discrete time adaptive PID control systems with parallel feedforward compensator , 2010 .

[12]  Jun Wu,et al.  A modeling and control approach to magnetic levitation system based on state-dependent ARX model , 2014 .

[13]  Mohd Azlan Hussain,et al.  Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation , 2011 .

[14]  Maciej Ławryńczuk,et al.  Accuracy and computational efficiency of suboptimal nonlinear predictive control based on neural models , 2011 .

[15]  Yu Cheng,et al.  Seasonal and trend time series forecasting based on a quasi-linear autoregressive model , 2014, Appl. Soft Comput..

[16]  Hui Peng,et al.  Quad-rotor modeling and attitude control using state-dependent ARX type model , 2014 .

[17]  Héctor Pomares,et al.  Time series analysis using normalized PG-RBF network with regression weights , 2002, Neurocomputing.

[18]  Lorenzo Fagiano,et al.  Adaptive receding horizon control for constrained MIMO systems , 2014, Autom..

[19]  Damiano Rotondo,et al.  A virtual actuator and sensor approach for fault tolerant control of LPV systems , 2014 .

[20]  Yukihiro Toyoda,et al.  An Akaike State-Space Controller for RBF-ARX Models , 2009, IEEE Transactions on Control Systems Technology.

[21]  Maciej Ławryńczuk,et al.  Computationally Efficient Model Predictive Control Algorithms , 2014 .

[22]  Kazushi Nakano,et al.  Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application , 2007, IEEE Transactions on Control Systems Technology.

[23]  Serdar Iplikci,et al.  A support vector machine based control application to the experimental three-tank system. , 2010, ISA transactions.

[24]  C. L. Philip Chen,et al.  Gradient Radial Basis Function Based Varying-Coefficient Autoregressive Model for Nonlinear and Nonstationary Time Series , 2015, IEEE Signal Processing Letters.

[25]  Tor Arne Johansen,et al.  Approximate explicit receding horizon control of constrained nonlinear systems , 2004, Autom..

[26]  Binoy Krishna Roy,et al.  Dual mode adaptive fractional order PI controller with feedforward controller based on variable parameter model for quadruple tank process. , 2016, ISA transactions.

[27]  Jun Wu,et al.  Ship's tracking control based on nonlinear time series model , 2012 .

[28]  Jun Wu,et al.  Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model☆ , 2009 .