A New Data-Driven Model-Free Adaptive Control for Discrete-Time Nonlinear Systems

Abstract

The existing model-free adaptive control encounters problems, such as too many parameters that need to be determined, some of which with unclear physical significance and whose selection depend entirely on trial and error. Aiming at this problem, a new dynamic linearized model is established by using Taylor series expansion of discrete-time nonlinear systems and the differential mean value theorem. Then, a new data-driven model-free adaptive control is proposed, which reduces the required parameters from six in the existing model-free adaptive control to four in the new model-free adaptive control. All the parameters have clear physical significance, and the selections of the initial values of the parameters are based on the stability conditions of the closed-loop system. Therefore, the selection of the parameters in the new modelfree adaptive control does not depend entirely on trial and error but on regularity. By introducing the idea of internal model control in the new model-free adaptive control, the anti-disturbance performance of the closed-loop system is enhanced. Finally, simulation results for three complicated nonlinear systems show that the proposed model-free adaptive control is superior to the existing model-free adaptive control.INDEX TERMS Model-free adaptive control, data-driven, discrete-time systems, nonlinear control. I. INTRODUCTIONCHUNHUA YANG received the M.Eng. degree in automatic control engineering and the Ph.D. degree in control science and engineering from

Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

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“…The above control method is a new model free adaptive control (NMFAC) by [32]. For a robotic hand system, Assumption 1 can be hardly satisfied.…”

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Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

“…There are many studies [4], including virtual reference feedback tuning (VRFT) [5][6][7], fictitious reference iterative tuning (FRIT) [8,9], model-free adaptive control based on dynamiclinearization techniques (MFAC-DLT) [10], model-free control based on an ultra-local model (MFC-ULM) [11,12], active disturbance rejection control (ADRC) [13], and adaptive fuzzy control (AFC) [14]. MFAC-DLT, MFC-ULM, and AFC can be applied to nonlinear systems and have been widely studied [10][11][12][13][14][15][16][17][18][19][20]. Additionally, control system design methods that do not use system models to be controlled are being applied to industrial systems, such as process systems and automotive systems [21][22][23][24][25][26][27].…”

Novel Sliding Mode Vibration Controller With Simple Model-Free Design and Compensation for Actuator’s Uncertainty

“…This approach constructs the controller using input and output data of the plant. For example, a data-driven control approach using a Taylor series and the differential mean value theorem for a discrete-time nonlinear system has been proposed [19]. However, the data-driven control method has numerous tuning parameters and imposes a heavy calculation burden.…”