Design and Analysis of Adaptive Control Systems Over Wireless Networks

Albattat, Ali; Gruenwald, Benjamin C.; Yucelen, Tansel

doi:10.1115/1.4035094

Cited by 12 publications

(3 citation statements)

References 30 publications

(42 reference statements)

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“…Unlike the above control methods, an aperiodic control technique called event-triggered control is presented in recent years [25][26][27][28][29][30][31][32]. The sampling and controlling of the systems only occur when the predefined events are true in event-triggered controls, such that the two-way data transmissions can be reduced.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the network traffic, several event-triggered control approaches have been developed in [25][26][27][28][29][30][31][32]. In [25,27], event-triggered adaptive control methods were introduced to schedule data exchange dependent upon errors exceeding user-defined thresholds to reduce wireless network utilization. In [26], an approximation-based event-triggered control method was proposed for multi-input multi-output uncertain nonlinear continuous-time systems in affine form.…”

Section: Introductionmentioning

confidence: 99%

“…The instants are determined by the proposed event-triggered mechanism to degrade the network traffic. Secondly, compared with the control methods presented in [2,16,17,19,22,25], where the input constraints of ASV are neglected, the proposed method takes input constraints into consideration and presents a saturated kinetic control law. The outputs of the controller are bounded, and the bound is known as a priori by employing a saturated function and a projection operator.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Model Reference Adaptive Control

Yucelen

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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Adaptive control methods present effective system‐theoretical tools in order to achieve closed‐loop system stability and performance in the presence of exogenous disturbances and system uncertainties, where they are generally classified as either direct or indirect. A well‐known class of direct adaptive control methods is model reference adaptive control architectures. In particular, these architectures employ two major components – a reference model and a parameter adjustment mechanism. A desired closed‐loop dynamical system response is captured by the reference model for which its response is compared with the response of the uncertain dynamical system. The system error signal resulting from this comparison drives the parameter adjustment mechanism. This mechanism then adjusts the controller parameters in a real‐time (i.e., online) manner for driving the trajectories of the uncertain dynamical system to the trajectories of the reference model. This article discusses these components in a basic state feedback setting in order to provide an introduction to the model reference adaptive control design procedure. The article also makes connections to several other, relatively advanced model reference adaptive control methods for interested readers.

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