Observer-Based Adaptive Fuzzy Backstepping Dynamic Surface Control for a Class of MIMO Nonlinear Systems

By Shaocheng Tong, Yong-ming Li, Gang Feng, Tie-shan Li. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011

461 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ee8c643d55ab02d480741a044f6c6b654308a659

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Citations in the corpus

Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation (2016, methodology): “However, the aforementioned control approaches in [1]–[24] assume that the considered nonlinear systems do not contain the input saturation [25], [26].”
Finite-Time Adaptive Fuzzy Output Feedback Dynamic Surface Control for MIMO Nonstrict Feedback Systems (2019, unknown): “According to [29] and [32], a new state variable α i,2 can be introduced, then, let αi,1 through a first-order filter with a time constant λi,2 to obtain α i,2 as follows: λi,2 α̇ c i,2 + α c i,2 = αi,1 , α c i,2(0) = αi,1(0).”
Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form (2014, unknown): “The observer-based adaptive fuzzy back-stepping DSC approach was developed for MIMO nonlinear systems, stochastic nonlinear strict-feedback systems, and uncertain nonlinear largescale systems with immeasurable states in [24]–[27], respectively, while the corresponding stability of the closed-loop sy”
Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints (2021, background): “Remark 1: If the states in system (1) are not available for measurement, it is a typical strict-feedback nonlinear system, for which many intelligent adaptive backstepping output feedback control design approaches has been given [7]–[10], [26], [27], [44], [45].”
Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems (2016, background): “2540058 [22] is for a class of MIMO nonlinear systems, while the work in [23] and [24] is for a class of nonlinear large-scale systems.”
Fuzzy Adaptive Output Feedback Control of MIMO Nonlinear Systems With Partial Tracking Errors Constrained (2015, methodology): “,mj , is called error surface; θj,ij is a state variable, which is obtained through a firstorder filter on intermediate control function αj,ij −1 ; and χj,ij is called the output error of the first-order filter [17]–[19].”
Adaptive Fuzzy Control via Observer Design for Uncertain Nonlinear Systems With Unmodeled Dynamics (2013, methodology): “Specifically, the results in [48], [50], [52], and [53] indicated that the controller design of uncertain large-scale nonlinear systems is still a difficult problem and is still open in the control area.”
Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems (2015, methodology): “Remark 6: It should be noted that the fuzzy output feedback DSC method proposed in [37] takes no account of the problem of compensating the errors caused by the filters; however, our proposed method introduces the filtering error compensation signals in this paper.”
Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System (2016, unknown): “Observer-based adaptive fuzzy backstepping dynamic surface control was proposed for a class of MIMO nonlinear systems in [62].”
Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems (2012, background): “Remark 6: It is worth mentioning that an observer-based adaptive fuzzy backstepping control approach has been recently developed in [24] and [25] for a class of uncertain MIMO strictfeedback nonlinear system (65), where fj,ij (xj,ij , xj,ij +1) = f̄j,ij (xj,ij ) + xj,ij +1 and fj,mj (X,uj ) = f̄j,m ”
Adaptive dynamic surface asymptotic tracking for a class of uncertain nonlinear systems (2018, background): “To further show the effectiveness of the proposed algorithm, a marine surface vehicle is taken into consideration as follows69: φ̈ + K T H(φ̇) = K T δ, (42) where φ is the yaw angle, δ is the actuator angle, K and T are hydrodynamic coefficients that can be obtained from trials in calm water, and H(”