Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach

Yang, Chenguang; Ge, Shuzhi Sam; Chen, Xiang; Chai, Tianyou; Lee, Tong Heng

doi:10.1109/tnn.2008.2003290

Cited by 206 publications

(8 citation statements)

References 42 publications

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“…The latter makes system operation very difficult, deteriorate the performance of the feedback-based control function and can even lead to instability [23,24]. Due to their ability to stabilize structured and unstructured uncertain systems, fuzzy control and neural network approaches are considered as powerful instruments for the control of the robotic systems [25][26][27][28][29]. In [11], an adaptive controller is introduced which is capable of synchronization in the presence of dynamic uncertainties without the appearance of any delay in information from the communication channel.…”

Section: Introductionmentioning

confidence: 99%

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Bouteraa

Alattas

Peng

et al. 2023

IET Control Theory & Appl

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In this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.

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Section: Introductionmentioning

confidence: 99%

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Bouteraa

Alattas

Peng

et al. 2023

IET Control Theory & Appl

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“…So far, the research in this aspect has advanced significantly. [13,14,[31][32][33][34] Generally the adaptive NN control scheme for nonlinear uncertain discrete-time systems is on the basis of the backstepping technique and Lyapunov stability theory. [20][21][22][23] For example, in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Theorem 1 Taking the second-order nonlinear system depicted in (1)- (3) into consideration, we provide the control law as (32) and the adaptation laws as (33). Then, under any bounded initial conditions, i.e.,ξ m (0) is initialized in Ω, all the closed-loop system signals preserve SGUUB and a small tracking error tolerance can be achieved through appropriate selection of control parameters.…”

Section: Trough the Introduction Of The Error Variablementioning

confidence: 99%

Adaptive control for uncertain discrete-time systems with unknown disturbance based on RNN

Cui

Guo

Feng

et al. 2016

AIR

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A new robust adaptive control algorithm is developed for a class of uncertain discrete-time SISO systems. Different from the existing investigated systems, the concerned discrete system here is with both uncertain smooth nonlinear functions and unknown disturbance. On the basis of the idea of neural network (NN) approximation, a novel recurrent neural network (RNN) is first proposed and used to approximate a backstepping control law following the transformation of the original system into a predictor form. According to Lyapunov stability theorem, a new on-line tuning law for parameters of RNN is obtained. Meanwhile, in order to achieve satisfying robust tracking performance, a novel controller is constructed by virtue of the approximation error of RNN. It has been proved that all the concerned signals are uniformly ultimately bounded. In addition, a very small tracking error can be obtained through appropriate selection of control parameters. Finally, we give a simulation example to demonstrate the validness of the newly proposed control algorithm for the investigated systems.

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“…In many existing works, universal function approximators (such as fuzzy systems and neural networks) are employed to tackle the unknown system uncertainty in nonlinear systems. On the basis of the output of universal function approximators, lots of robust adaptive control schemes were designed for the uncertain MIMO nonlinear system [20][21][22][23][24]. In [17], robust adaptive sliding mode control was proposed using fuzzy modelling for a class of uncertain MIMO nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control for a Class of Uncertain MIMO Nonlinear Systems with Application to Near-Space Vehicles

Mei

Jiang

2013

Mathematical Problems in Engineering

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We propose a robust sliding mode control (SMC) scheme for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with the unknown external disturbance, the system uncertainty, and the backlash-like hysteresis. To tackle the continuous system uncertainty, the radial basis function (RBF) neural network is employed to approximate it. And then, combine the unknown external disturbance, and the unknown neural network approximation error with the affection caused by backlash-like hysteresis as a compounded disturbance which is estimated using the developed nonlinear disturbance observer. The robust sliding mode control based on the nonlinear disturbance observer and RBF neural network is presented to track the desired system output in the presence of the unknown system uncertainty, the external disturbance, and the backlash-like hysteresis. Finally, the designed robust sliding mode control strategy is applied to the near-space vehicle (NSV) attitude dynamics, and simulation results are given to illustrate the effectiveness of the proposed sliding mode control approach.

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Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach

Cited by 206 publications

References 42 publications

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Adaptive control for uncertain discrete-time systems with unknown disturbance based on RNN

Sliding Mode Control for a Class of Uncertain MIMO Nonlinear Systems with Application to Near-Space Vehicles

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