Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system

Gao, Ying; Liu, Yan Jun

doi:10.1177/1077546314534286

Cited by 90 publications

(42 citation statements)

References 31 publications

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“…Remark 3: In this paper, RBF neural networks are employed due to its capabilities of approximating any unstructured smooth nonlinear functions to arbitrary accuracy over a compact set. In fact, one can use other online approximation method instead, such as fuzzy logic systems [43], [44], [45], [32]. The online approximator can be further reduced to a regressor function by assuming that the uncertainties are structured and are linear in parameters [46], which requires a set of model-specific basis functions.…”

Section: Preliminaries: Neural Networkmentioning

confidence: 99%

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method.

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Section: Preliminaries: Neural Networkmentioning

confidence: 99%

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…Various robust adaptive fuzzy sliding mode controllers have been presented for a class of nonlinear fractional-order systems with unknown control direction [6,9,38,52]. In [2,10,11,[47][48][49][50][51][60][61][62][63], five methods have been used to cope with the unknown control direction problem: (1) a method based on a Nussbaum-type function, (2) a method based on directly estimating unknown parameters, (3) a method based on a monitoring function, (4) a method based on a hysteresis-type function, and (5) a method based on a hysteresis dead-zone-type function and a Nussbaum function. Compared with the existing controls in [2,8,10,11,40,62], the adaptive fuzzy control laws presented in [47][48][49] have solved the tracking problem for nonlinear uncertain discrete-time systems with unknown control direction and input nonlinearities (such as dead zone, backlash-like hysteresis, and backlash), by using the reinforcement learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

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This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input-multioutput nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup property of fractional-order derivative, the system is transformed into a normalized fractional-order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real condition, the Razumikhin Lemma, the frequency-distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof. The main advantages of this work are that:(1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

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“…In [21]- [23], adaptive fuzzy control was used for identification of the unknown nonlinear control system. Fuzzy control was used for studying uncertain nonlinear systems in [24], [25]. In [26], fuzzy control was adopted to improve the performance of the automobile cruise system.…”

Section: Introductionmentioning

confidence: 99%

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Yang

Wang

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

309

169

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Abstract-In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

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Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system

Cited by 90 publications

References 31 publications

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

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