High-Gain Observer-Based Sliding-Mode Dynamic Surface Control for Particleboard Glue Mixing and Dosing System

In the process of particleboard glue mixing and dosing control under the working condition of intermediate frequency, a sliding-mode dynamic surface control strategy based on high-gain observer is proposed in this paper to deal with the problem of glue flow stability caused by strong nonlinearity. The high-gain observer (HGO) is aimed at estimating the derivative of the immeasurable system input signal for feedback, and the robustness of the system is improved by the dynamic surface control (DSC) method. Furthermore, the sliding-mode control (SMC) method is used to deal with disturbances caused by the uncertainties as well as external disturbances. It is proven that the system is exponential asymptotic stable by constructing a suitable Lyapunov function. Simulation results show that the proposed control methods can make the system track the expected flow value quickly and accurately. Finally, numerical simulation results are exhibited to authenticate and validate the effectiveness of the proposed control scheme.

[1]  Chenguang Yang,et al.  Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Frank L. Lewis,et al.  Dynamic Neural Network-Based Robust Backstepping Control approach for Quadrotors , 2008 .

[3]  Samir Ladaci,et al.  Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos , 2018, Algorithms.

[4]  Shaocheng Tong,et al.  Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[5]  Vincent Cocquempot,et al.  Adaptive fault‐tolerant backstepping control against actuator gain faults and its applications to an aircraft longitudinal motion dynamics , 2013 .

[6]  A. Hamidat,et al.  Mathematic models of photovoltaic motor-pump systems , 2008 .

[7]  Hao Wang,et al.  Neural network based adaptive dynamic surface control for cooperative path following of marine surface vehicles via state and output feedback , 2014, Neurocomputing.

[8]  Hasan Komurcugil,et al.  Time-Varying and Constant Switching Frequency-Based Sliding-Mode Control Methods for Transformerless DVR Employing Half-Bridge VSI , 2017, IEEE Transactions on Industrial Electronics.

[9]  Changyin Sun,et al.  Finite time integral sliding mode control of hypersonic vehicles , 2013 .

[10]  C. L. Philip Chen,et al.  Decentralized Control for Second-Order Uncertain Nonlinear Multi-agent Systems Consensus Problem Based on Fuzzy Adaptive High-Gain Observer , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[11]  Qing Guo,et al.  High-gain observer-based output feedback control of single-rod electro-hydraulic actuator , 2015 .

[12]  M. Farza,et al.  A high gain observer coupled to a sliding mode technique for electropneumatic system control , 2014, 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[13]  Vineet Kumar,et al.  Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator , 2014, Expert Syst. Appl..

[14]  Ioan-Daniel Borlea,et al.  Model-Free Sliding Mode and Fuzzy Controllers for Reverse Osmosis Desalination Plants , 2018 .

[15]  Tong Heng Lee,et al.  Design and Implementation of Integral Sliding-Mode Control on an Underactuated Two-Wheeled Mobile Robot , 2014, IEEE Transactions on Industrial Electronics.

[16]  Antonio Visioli,et al.  Tuning rules for optimal PID and fractional-order PID controllers , 2011 .

[17]  Maolin Jin,et al.  Variable PID Gain Tuning Method Using Backstepping Control With Time-Delay Estimation and Nonlinear Damping , 2014, IEEE Transactions on Industrial Electronics.

[18]  Keum-Shik Hong,et al.  Control of linear motor machine tool feed drives for end milling: Robust MIMO approach , 1999 .

[19]  Michael Defoort,et al.  High-gain observer with sliding mode for nonlinear state estimation and fault reconstruction , 2014, J. Frankl. Inst..

[20]  Antonella Ferrara,et al.  Adaptive suboptimal second-order sliding mode control for microgrids , 2016, Int. J. Control.

[21]  Radu-Emil Precup,et al.  Model-free sliding mode control of nonlinear systems: Algorithms and experiments , 2017, Inf. Sci..

[22]  Qian Mo-sh Robust dynamics surface fault tolerant control design for attitude control systems of UAV , 2014 .

[23]  J. C. Gerdes,et al.  Dynamic surface control of nonlinear systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[24]  H. Jin Kim,et al.  Adaptive Dynamic Surface Control based on Neural Network for Missile Autopilot , 2011 .

[25]  Zhang Cong,et al.  Non-singular terminal dynamic surface control based integrated guidance and control design and simulation. , 2016, ISA transactions.

[26]  Marta Herva,et al.  Assessing environmental sustainability of particleboard production process by ecological footprint , 2013 .

[27]  R. Kozak,et al.  Particleboard performance requirements of secondary wood products manufacturers in Canada , 2008 .

[28]  Ching-Hung Lee,et al.  Fractional-order PID controller optimization via improved electromagnetism-like algorithm , 2010, Expert Syst. Appl..

[29]  Hassan K. Khalil,et al.  Application of the extended high gain observer to underactuated mechanical systems , 2012, 2012 American Control Conference (ACC).

[30]  Josep M. Guerrero,et al.  Performance Improvement of a Prefiltered Synchronous-Reference-Frame PLL by Using a PID-Type Loop Filter , 2014, IEEE Transactions on Industrial Electronics.

[31]  Paolo Mercorelli,et al.  A Two-Stage Sliding-Mode High-Gain Observer to Reduce Uncertainties and Disturbances Effects for Sensorless Control in Automotive Applications , 2015, IEEE Transactions on Industrial Electronics.