A novel adaptive control approach for nonlinearly parameterized systems

Chen, Zhiyong

doi:10.1002/acs.2462

Cited by 13 publications

(3 citation statements)

References 38 publications

(40 reference statements)

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“…In [19,20], fuzzy logic systems were employed to estimate the lumped unknown functions. In [21][22][23][24], adaptive techniques were used to reject the model uncertainties and compensate the effects of external disturbances. Nevertheless, the above systems work with periodic sampling and controlling in these studies , known as periodic-triggered control systems.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered neural network control of autonomous surface vehicles over wireless network

Wang

Peng

et al. 2020

Sci. China Inf. Sci.

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In this paper, an event-triggered neural network control method is proposed for autonomous surface vehicles subject to uncertainties and input constraints over wireless network. An event-triggered mechanism with three logic rules is employed to determine the wireless data transmission of states and control inputs. An event-driven neural network is applied to approximate the uncertainties using aperiodic sampled states. In addition, a predictor is employed to update the weights of neural network. An event-based bounded kinetic control law is applied to address the actuator constraints. The advantage of the proposed event-triggered neural network control approach is that the network traffic can be reduced while guaranteeing system stability and speed following performance. The closed-loop control system is proved to be input-tostate stable via cascade theory. The Zeno behavior can be avoided via the proposed event-triggered neural network control approach. A simulation example is provided to demonstrate the effectiveness of the proposed event-triggered neural network control approach for autonomous surface vehicles.

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Section: Introductionmentioning

confidence: 99%

Event-triggered neural network control of autonomous surface vehicles over wireless network

Wang

Peng

et al. 2020

Sci. China Inf. Sci.

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“…As a pioneering work, Kanellakopoulos et al first proposed the famous integrator backstepping to systematically solve the adaptive control problem for nonlinear systems in parametric strict‐feedback form. Later, the method of overcoming the drawback of overparameterization was given in the work of Krstić et al Since then, a series of stabilizing schemes involving nonlinear and adaptive control were well documented in other works and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite‐time stabilization of nonlinearly parameterized systems subject to mismatching disturbances

Min

Zhang

2019

Intl J Robust & Nonlinear

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This paper gives a first try to the finite-time control for nonlinear systems with unknown parametric uncertainty and external disturbances. The serious uncertainties generated by unknown parameters are compensated by skillfully using an adaptive control technique. Exact knowledge of the upper bounds of the disturbances is removed by employing a disturbance observer-based control method. Then, based on the disturbance observer-based control, backstepping technique, finite-time adaptive control, and Lyapunov stability theory, a composite adaptive state-feedback controller is strictly designed and analyzed, which guarantees the closed-loop system to be practically finite-time stable. Finally, both the practical and numerical examples are presented and compared to demonstrate the effectiveness of the proposed scheme.

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“…1 Compared with the traditional adaptive control, the most appealing merit of NNAC is that the modelling difficulty in many practical control problems can be greatly mitigated resulting in the simplification of control synthesis for a wider class of nonlinear systems with functional uncertainties. 2 However, in most existing NNAC methods, eg, see some recent works, [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] the ability of NNs to learn plant uncertainties is not completely exploited and only tracking error convergence is available. The ability to learn for NNs, reflected by the convergence of NN weights, is guaranteed by the well-known condition termed persistent excitation (PE).…”

Section: Introductionmentioning

confidence: 99%

Composite learning adaptive backstepping control using neural networks with compact supports

Yang

Pratama

2019

Adaptive Control & Signal

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The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed-loop system is established under a weak excitation condition termed interval excitation.The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised-cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.

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A novel adaptive control approach for nonlinearly parameterized systems

Cited by 13 publications

References 38 publications

Event-triggered neural network control of autonomous surface vehicles over wireless network

Event-triggered neural network control of autonomous surface vehicles over wireless network

Adaptive finite‐time stabilization of nonlinearly parameterized systems subject to mismatching disturbances

Composite learning adaptive backstepping control using neural networks with compact supports

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