On Event Based State Estimation

Sijs, Joris; Lazăr, Mircea

doi:10.1007/978-3-642-00602-9_24

Cited by 55 publications

(33 citation statements)

References 21 publications

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“…Event-based state estimation is considered in [21]. In that paper a state estimator adopts a hybrid update scheme in the sense that updates take place both when an event occurs that triggers the transmission of new measurements to the estimator (asynchronous times), as well as when a periodic timer expires (synchronous times).…”

Section: A Continuous-time Observer-based Event-triggered Controlmentioning

confidence: 99%

“…Hence, receiving no information at a synchronous time instant indicates that the output is still in this bounded set, which is information that can be used to guarantee bounded estimation error covariances. In fact, in [21] this is formally shown based on a sum-of-Gaussians approach that is used to obtain a computationally tractable algorithm. An example of integrating this event-based state estimator with a periodically time-triggered control algorithm is provided in [25].…”

Section: A Continuous-time Observer-based Event-triggered Controlmentioning

confidence: 99%

“…In most cases the observer reconstructs the plant state using solely event-based information in the sense that it only applies innovation steps exploiting received measurements at the event times, although some schemes also exploit the information present at synchronous (time-triggered) instants of time at which no events occur. The latter is, for instance, the case in [19], [20], [21]. In fact, [19] states literally "absence of an event is however information that can be used by the observer," see also [22].…”

mentioning

confidence: 99%

“…In fact, [19] states literally "absence of an event is however information that can be used by the observer," see also [22]. The observer-based schemes can still be categorized further based on the fact if the corresponding event-triggering conditions use information of the observer such as the estimated state, as, e.g., in [20], [23], [24] or not [21], [25]. The former schemes typically run the observer at the sensor side using essentially all measurements available, while at the controller side a predictor-like structure also produces a state estimate, which is based on only sporadically received information from the sensor system (including the observer) based on the event-triggering mechanisms.…”

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confidence: 99%

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An introduction to event-triggered and self-triggered control

Heemels

Johansson

Tabuada

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

1,618

952

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Abstract-Recent developments in computer and communication technologies have led to a new type of large-scale resource-constrained wireless embedded control systems. It is desirable in these systems to limit the sensor and control computation and/or communication to instances when the system needs attention. However, classical sampled-data control is based on performing sensing and actuation periodically rather than when the system needs attention. This paper provides an introduction to event-and self-triggered control systems where sensing and actuation is performed when needed. Eventtriggered control is reactive and generates sensor sampling and control actuation when, for instance, the plant state deviates more than a certain threshold from a desired value. Selftriggered control, on the other hand, is proactive and computes the next sampling or actuation instance ahead of time. The basics of these control strategies are introduced together with a discussion on the differences between state feedback and output feedback for event-triggered control. It is also shown how event-and self-triggered control can be implemented using existing wireless communication technology. Some applications to wireless control in process industry are discussed as well.

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Section: A Continuous-time Observer-based Event-triggered Controlmentioning

confidence: 99%

Section: A Continuous-time Observer-based Event-triggered Controlmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

An introduction to event-triggered and self-triggered control

Heemels

Johansson

Tabuada

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

1,618

952

View full text Add to dashboard Cite

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“…A fundamental challenge with deterministic send-on-delta approaches is that they involve nonlinear measurements, which implies that the design of an optimal state estimator becomes intractable for higher-order systems [6], [7]. While approximative estimation methods such as particle filters exist, these typically involve very heavy on-line computations and do not easily lend themselves to analysis.…”

mentioning

confidence: 99%

Event-based state estimation using an improved stochastic send-on-delta sampling scheme

Andrén

Cervin

2016

2016 Second International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)

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Abstract-Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in the stochastic triggering scheme, the optimal remote state estimator becomes a linear Kalman filter with a case dependent measurement update. In this paper we propose a modified version of the stochastic send-on-delta triggering rule. The idea is to use a very simple predictor in the sensor, which allows the communication rate to be reduced while preserving estimation performance compared to regular stochastic send-on-delta sampling. We derive the optimal meansquare error estimator for the new scheme and present upper and lower bounds on the error covariance. The proposed scheme is evaluated in numerical examples, where it compares favorably to previous stochastic sampling approaches, and is shown to preserve estimation performance well even at large reductions in communication rate.

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Decentralized control of network‐based interconnected systems: A state‐dependent triggering method

Tian

Yue

2013

Intl J Robust & Nonlinear

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This paper investigates decentralized control for a class of interconnected system. Different from the traditional systems, the considered system has the following features: (i) its subsystems are connected through a communication network subject to transmission delays and packet losses; (ii) the subsystems' multi-actuators are subjected to random faults; and (iii) the subsystems are subject to probabilistic nonlinear disturbances, the inner variation information of the nonlinearities, as well as their bounds information, is utilized to analyze the nonlinearities. Furthermore, in order to reduce the network bandwidth burden, a decentralized state-dependent triggering scheme is proposed. Considering aforementioned characteristics and using the state-dependent triggering scheme, new type of network-based interconnected system model is built. By using the Lyapunov functional approach, sufficient conditions for the mean square stability and stabilization of the network-based interconnected systems are obtained. Then reliable controllers, as well as the triggering matrices of the local subsystems, can be co-designed by using a cone complementary linearization algorithm. Two simulation examples are given to illustrate the effectiveness and application of the proposed method. 1127 decentralized reliable guaranteed cost control of interconnected system subject to actuator faults. In [13], a decentralized adaptive approximation design method is proposed for the fault tolerant control of a class of interconnected nonlinear system. However, when considering all these three issues at the same time, the related research is not enough thus still need further investigation.It is well known that limited communication capacity is one of the main sources of the system performance degradation in the networked control systems, which can deteriorate the first two issues described in the previous texts. As to the limited network bandwidth, the following two methods are usually used to reduce the network bandwidth load: (i) the first is to reduce the size of the data. For example, in [14,15], the authors investigate the quantized feedback stabilization of interconnected systems. Generally speaking, the quantized method can only reduce the size of the data while can not reduce the quantity of the data, then the throughput capacity of the network can not be eased effectively, especially when the number of subsystems is large. (ii) The second method is to reduce the quantity of the released data. State-dependent triggering scheme (SDTS) (also called eventtriggering scheme or self-triggering scheme) has been proved to be an efficient way to reduce the data transmission in the network [16][17][18][19][20], which can relieve the burden of network bandwidth occupation when compared with traditional periodic sampling method. In the periodic sampling scheme, the sampling interval is determined before the system is deployed and which is computed to ensure the system performance of the worst conditions. Consequently, the obtained sampling interva...

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On Event Based State Estimation

Cited by 55 publications

References 21 publications

An introduction to event-triggered and self-triggered control

An introduction to event-triggered and self-triggered control

Event-based state estimation using an improved stochastic send-on-delta sampling scheme

Decentralized control of network‐based interconnected systems: A state‐dependent triggering method

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