A Novel Model-Free Adaptive Control Design for Multivariable Industrial Processes

In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.

[1]  Kevin M. Passino,et al.  Stable Adaptive Control and Estimation for Nonlinear Systems , 2001 .

[2]  Di Wu,et al.  STABILITY ANALYSIS FOR SAMPLED-DATA SYSTEMS BASED ON MULTIPLE LYAPUNOV FUNCTIONAL METHOD , 2012 .

[3]  Steven X. Ding,et al.  Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization , 2014, IEEE Transactions on Industrial Electronics.

[4]  Liu Xiaodong,et al.  H/sub /spl infin// controller designs based on fuzzy observers for T-S fuzzy systems via LMI , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[5]  Yujing Shi,et al.  Nonlinear Multivariable Decoupling PID Control Using Neural Networks , 2005, 2005 International Conference on Neural Networks and Brain.

[6]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[7]  Huijun Gao,et al.  State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems , 2010, IEEE Transactions on Automatic Control.

[8]  L. Xiaodong,et al.  New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI , 2003, Autom..

[9]  Tong Shao-cheng,et al.  Fuzzy adaptive observer backstepping control for MIMO nonlinear systems , 2009 .

[10]  Shaocheng Tong,et al.  Observer-based fuzzy adaptive control for strict-feedback nonlinear systems , 2009, Fuzzy Sets Syst..

[11]  Jianqiang Yi,et al.  ENCODING PRIOR KNOWLEDGE INTO DATA DRIVEN DESIGN OF INTERVAL TYPE-2 FUZZY LOGIC SYSTEMS , 2011 .

[12]  R. K. Wood,et al.  Terminal composition control of a binary distillation column , 1973 .

[13]  Steven X. Ding,et al.  Data-driven monitoring for stochastic systems and its application on batch process , 2013, Int. J. Syst. Sci..

[14]  Karl Henrik Johansson,et al.  Design of decoupled PI controllers for two-by-two systems , 2002 .

[15]  Antonio Sala,et al.  Extensions to "virtual reference feedback tuning: A direct method for the design of feedback controllers" , 2005, Autom..

[16]  Xiaodong Liu,et al.  New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI , 2003, Autom..

[17]  Huaguang Zhang,et al.  Optimal Tracking Control for a Class of Nonlinear Discrete-Time Systems With Time Delays Based on Heuristic Dynamic Programming , 2011, IEEE Transactions on Neural Networks.

[18]  Shengyuan Xu,et al.  Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[20]  Shiuh-Jer Huang,et al.  A hybrid fuzzy logic and neural network algorithm for robot motion control , 1997, IEEE Trans. Ind. Electron..

[21]  Derong Liu,et al.  Adaptive Dynamic Programming for Control: Algorithms and Stability , 2012 .

[22]  Shaocheng Tong,et al.  Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems , 2012, IEEE Transactions on Fuzzy Systems.

[23]  Seung-Ki Sul,et al.  Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle , 1998, IEEE Trans. Ind. Electron..

[24]  R. Vilanova,et al.  DATA-DRIVEN BASED IMC CONTROL , 2011 .

[25]  S. S. Ge,et al.  Adaptive NN control for a class of discrete-time non-linear systems , 2003 .

[26]  Shaocheng Tong,et al.  Fuzzy adaptive sliding-mode control for MIMO nonlinear systems , 2003, IEEE Trans. Fuzzy Syst..

[27]  Yongduan Song,et al.  A Novel Control Design on Discrete-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delays , 2013, IEEE Transactions on Fuzzy Systems.

[28]  Shangtai Jin,et al.  A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Control Systems Technology.

[29]  Shaocheng Tong,et al.  Robust fuzzy decentralized control for nonlinear large-scale systems with parametric uncertainties , 2008, J. Intell. Fuzzy Syst..

[30]  Yongduan Song,et al.  ${\cal H}_{\infty}$ Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Shangtai Jin,et al.  Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems , 2011, IEEE Transactions on Neural Networks.

[32]  Ligang Wu,et al.  Observer-based sliding mode control for a class of uncertain nonlinear neutral delay systems , 2008, J. Frankl. Inst..