Model Based Robust Control Law for Linear Event‐Triggered System

Tripathy, Niladri Sekhar; Kar, Indra Narayan; Paul, Kolin

doi:10.1002/asjc.1276

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…In order to further expand interexecution times (IETs) of the general ETC which is usually called static ETC, a dynamic event‐triggered mechanism (ETM) strategy was proposed in Reference 23, where the relationship between the minimum interexecution time (MIET) and the system matrix is quiet complicated. Based on the input‐to‐state stability theory, the dynamic ETM was applied to the control of linear uncertain systems in References 24 and 25. However, the exact relationship between the parameters and MIET was not discussed in References 24 and 25.…”

Section: Introductionmentioning

confidence: 99%

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Parametric Lyapunov equation based event‐triggered and self‐triggered control of input constrained linear systems

Zhang

Zhou

Jiang

2020

Intl J Robust & Nonlinear

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This article studies semiglobal stabilization of input constrained linear systems based on the static and dynamic event-triggered control (ETC) and self-triggered control (STC). First, a static ETC based on the parametric Lyapunov equation (PLE) is designed. Then the corresponding dynamic ETC is also proposed. The main advantage of the proposed static and dynamic ETC algorithms is that the minimum interexecution time (MIET) as a decreasing function of the parameter in the PLE is given, which allows us to find easily a trade-off between the interexecution times (IETs) and the control performance. The fact that the MIET of the dynamic ETC is never less than that of the static ETC is also guaranteed. Next, in order to avoid continuous monitoring of the states, both static and dynamic STC are designed. The influence of the parameters on IETs of the static and dynamic STC is analyzed in detail. The nonoccurrence of the Zeno phenomenon is proved in all the control algorithms. Finally, the proposed algorithms are applied to the design of spacecraft rendezvous. Numerical simulations on the original nonlinear model of the spacecraft rendezvous control system show the control system effectiveness of the algorithms.

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Section: Introductionmentioning

confidence: 99%

“…Based on the input‐to‐state stability theory, the dynamic ETM was applied to the control of linear uncertain systems in References 24 and 25. However, the exact relationship between the parameters and MIET was not discussed in References 24 and 25. Other results about the dynamic ETC and STC can be found in References 26, 27, 28, 29.…”

Section: Introductionmentioning

confidence: 99%

Parametric Lyapunov equation based event‐triggered and self‐triggered control of input constrained linear systems

Zhang

Zhou

Jiang

2020

Intl J Robust & Nonlinear

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“…To avoid infinitely fast sampling, they have proposed an Input-to-State Stability (ISS) gain condition and correspondingly an event and self-triggering mechanism subject to external disturbances. Recently, in [42], [43], an event-triggered robust control algorithm has been developed based on aperiodic feedback to deal with the presence of uncertainty, albeit limited to linear systems. Tripathy et al have adopted an optimal control strategy to design such a robust control law [42], [43].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, in [42], [43], an event-triggered robust control algorithm has been developed based on aperiodic feedback to deal with the presence of uncertainty, albeit limited to linear systems. Tripathy et al have adopted an optimal control strategy to design such a robust control law [42], [43]. Originally, this control law has been developed by Lin et al [22], [23] within the optimal control framework.…”

Section: Introductionmentioning

confidence: 99%

Robust Stabilization of a Class of Nonlinear Systems via Aperiodic Sensing and Actuation

et al. 2020

Self Cite

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This paper proposes a framework to design a robust controller for a class of nonlinear networked control systems using aperiodic feedback information. Here, the nonlinearity and parameter variations of system model are considered as sources of uncertainty. To tackle the uncertainty in system dynamics, a linear robust control law is derived by applying the optimal control theory. Two different architectures of closed-loop systems are considered. In the first one, system and controller are not collocated; instead they are interconnected by means of a shared communication network. In the second architecture, system, controller and actuator are all collocated with their respective outputs available at all time-instead, sensors and controller are connected through a shared communication channel. In both architectures, the feedback loop is closed through the network. Owing to its shared nature, the network may suffer from bandwidth limitations. To save the network bandwidth, state and input information are transmitted aperiodically within the feedback loop. With this aim, the paper adopts an event-triggered control technique so as to reduce the transmission overhead. Applying Input-to-State Stability theory, we derive two different event-triggered robust control laws that stabilize the uncertain nonlinear system. Finally, we show that the designed event-triggered controllers satisfy the trade-off between control performance and saving in network bandwidth in the presence of uncertainty. The developed control algorithm is implemented and validated through numerical simulations.

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“…However, using this strategy scheme for data processing results in a conservative usage of digital resources (see, for instance, [ 3 , 4 , 5 , 6 ] and references therein). Because sensors communicate frequently with these digital devices through limited bandwidth lines, especially in networked control systems, communication between sensors and the controller should be as small as possible to increase the performance of the closed-loop system against, for instance, congestion data or packet dropouts [ 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], among other benefits [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Event-Driven Observer-Based Smart-Sensors for Output Feedback Control of Linear Systems

Acho

2017

Sensors

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This paper deals with a recent design of event-driven observer-based smart sensors for output feedback control of linear systems. We re-design the triggering mechanism proposed in a previously reported system with the implementation of self-sampling data smart sensors; as a result, we improve its performance. Our approach is theoretically supported by using Lyapunov theory and numerically evidenced by controlling the inverted pendulum on the cart mechanism.

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Model Based Robust Control Law for Linear Event‐Triggered System

Cited by 17 publications

References 28 publications

Parametric Lyapunov equation based event‐triggered and self‐triggered control of input constrained linear systems

Parametric Lyapunov equation based event‐triggered and self‐triggered control of input constrained linear systems

Robust Stabilization of a Class of Nonlinear Systems via Aperiodic Sensing and Actuation

Event-Driven Observer-Based Smart-Sensors for Output Feedback Control of Linear Systems

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