A novel adaptive dynamic surface control (DSC) method for the micro-electromechanical systems gyroscope, which combined the approaches of a radial basis function neural networks (RBFNN) and a nonsingular terminal sliding mode (NTSM) controller was proposed in this paper. In the DSC, a first-order filter was introduced to the conventional adaptive backstepping technique, which not only maintains the advantage of original backstepping technique, but also reduces the number of parameters and avoids the problem of parameters expansion. The RBFNN is an approximation to the gyroscope’s dynamic characteristics and external disturbances. By introducing a nonsingular terminal sliding mode controller which ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. Finally, simulation results prove that the proposed approach could reduce the chattering of inputs, improve the timeliness and effectiveness of tracking in the presence of model uncertainties and external disturbances, demonstrating the excellent performance compared to nonsingular terminal sliding mode control (NTSMC).
Dynamic surface control (DSC) Radial basis function neural networks (RBFNN) Nonsingular terminal sliding mode control (NTSMC)
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The authors thank the anonymous reviewers for their useful comments that improved the quality of the paper. This work is partially supported by National Science Foundation of China under Grant No. 61374100; The Fundamental Research Funds for the Central Universities under Grant No. 2014B05014.
Liu Z, Aduba C, Won C (2011) In-plane dead reckoning with knee and waist attached gyroscopes. Measurement 10(6):1860–1868CrossRefGoogle Scholar
Tsai NC, Sue CY (2010) Experimental analysis and characterization of electrostatic-drive tri-axis micro-gyroscope. Sens Actuators A 2(3):231–239CrossRefGoogle Scholar
Yip PP, Hedrick JK (1998) Adaptive dynamic surface control: a simplified algorithm for adaptive backstepping control of nonlinear systems. Int J Control 71(5):959–979MathSciNetCrossRefMATHGoogle Scholar
Zhang TP, Ge SS (2008) Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44(7):1895–1903MathSciNetCrossRefMATHGoogle Scholar
Li TS, Wang D, Feng G, Tong SC (2010) A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybern Part B Cybern 40(3):915–927CrossRefGoogle Scholar
Chen WS (2009) Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks. IET Control Theory Appl 3(10):1383–1394MathSciNetCrossRefGoogle Scholar
Li Y, Tong S, Li T (2015) Adaptive fuzzy output feedback dynamic surface control of interconnected nonlinear pure-feedback systems. IEEE Trans Cybern 45(1):138–149CrossRefGoogle Scholar
Wang D, Huang J (2005) Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict feedback form. IEEE Trans Neural Netw 16(1):195–202CrossRefGoogle Scholar
Li Y, Tong S (2015) Prescribed performance adaptive fuzzy output-feedback dynamic surface control for nonlinear large-scale systems with time delays. Inf Sci 292:125–142MathSciNetCrossRefMATHGoogle Scholar
Yoo SJ, Park JB, Choi YH (2006) Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks. IEEE Trans Syst Man Cybern Part B Cybern 36(6):1342–1355CrossRefGoogle Scholar
Song ZK, Li HX, Sun KB (2014) Adaptive dynamic surface control for MEMS triaxial gyroscope with nonlinear inputs. Nonlinear Dyn 78(1):173–182CrossRefMATHGoogle Scholar