Design of Observers for Hybrid Systems

Balluchi, A.; Benvenuti, Luca; Benedetto, Maria Domenica Di; Sangiovanni‐Vincentelli, Alberto

doi:10.1007/3-540-45873-5_9

Cited by 220 publications

(145 citation statements)

References 16 publications

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“…This in turn implies that (a)R(q, y) ⊆ Rch(q) for all y ∈ Y andq ∈Q and that (b)τ 0 =τ 0 = 0 and y(τ 0 ) is such thatR(q(τ 0 ), y(τ 0 )) := Rch(q(τ 0 )) = Rch(q 0 ). The discrete input y(t) derives information from the measured continuous state signal about the values ofẋ(τ) for τ < t and utilizes this information to determine the current set of modes compatible with such a derivative (see [3,8,9] for more information on mode estimators).…”

Section: Definition 2 a Hidden Mode Hybrid System (Hmhs)mentioning

confidence: 99%

Control of Hidden Mode Hybrid Systems: Algorithm termination

Verma

Vecchio

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-We consider the problem of safety control in Hidden Mode Hybrid Systems (HMHS) that arises in the development of a semi-autonomous cooperative active safety system for collision avoidance at an intersection. We utilize the approach of constructing a new hybrid automaton whose discrete state is an estimate of the HMHS mode. A dynamic feedback map can then be designed that guarantees safety on the basis of the current mode estimate and the concept of the capture set. In this work, we relax the conditions for the termination of the algorithm that computes the capture set by constructing an abstraction of the new hybrid automaton. We present a relation to compute the capture set for the abstraction and show that this capture set is equal to the one for the new hybrid automaton.

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Section: Definition 2 a Hidden Mode Hybrid System (Hmhs)mentioning

confidence: 99%

Control of Hidden Mode Hybrid Systems: Algorithm termination

Verma

Vecchio

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…Work on identification/filtering of hybrid systems first appeared in the seventies (see [19] for a review). More recent works consider variations of Problem 1 in which the model parameters, the discrete state and/or the switching mechanism are known, and concentrate on the analysis of the observability of the hybrid state [2], [4], [9], [11], [18], [21], [22] and the design of hybrid observers [1], [3], [7], [8], [10], [12], [14], [16], [17], [20].…”

Section: Problemmentioning

confidence: 99%

Identification of PWARX hybrid models with unknown and possibly different orders

Vidal

2004

Proceedings of the 2004 American Control Conference

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Abstract-We consider the problem of identifying the orders and the model parameters of PWARX hybrid models from noiseless input/output data. We cast the identification problem in an algebraic geometric framework in which the number of discrete states corresponds to the degree of a multivariate polynomial p and the orders and the model parameters are encoded on the factors of p. We derive a rank constraint on the input/output data from which one can estimate the coefficients of p. Given p, we show that one can estimate the orders and the parameters of each ARX model from the derivatives of p at a collection of regressors that minimize a certain objective function. Our solution does not require previous knowledge about the orders of the ARX models (only an upper bound is needed), nor does it constraint the orders to be equal. Also the switching mechanism can be arbitrary, hence the switches need not be separated by a minimum dwell time. We illustrate our approach with an algebraic example of a switching circuit and with simulation results in the presence of noisy data.

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“…In distributed hybrid systems, the state s t is the aggregate state of all the subsystems {s (1) t , s (2) t , . .…”

Section: Problem Statementmentioning

confidence: 99%

“…The design parameter depends on the process and measurement noise. The transition probability matrix can be represented by the transformed guard conditions by equation (1). This method has been used also for removing Zeno behavior from hybrid system models.…”

Section: Hybrid System Estimationmentioning

confidence: 99%

“…A related approach to our work based on banks of extended Kalman filters has been presented in [4] where only trajectories with high confidence probability are traced. A methodology using both discrete and continuous observers based on finite state machines and linear systems has been proposed in [1]. Sequential Monte Carlo (or particle filtering) methods can support process densities that contain both continuous and discrete dynamics and have been explored for hybrid diagnosis in [11].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Distributed Hybrid Systems Using Particle Filtering Methods

Koutsoukos

Kurien

Zhao

2003

Lecture Notes in Computer Science

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Abstract. Networked embedded systems are composed of a large number of components that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Such systems are best modeled by distributed hybrid systems that capture the interaction between the physical and computational components. Monitoring and diagnosis of any dynamical system depend crucially on the ability to estimate the system state given the observations. Estimation for distributed hybrid systems is particularly challenging because it requires keeping track of multiple models and the transitions between them. This paper presents a particle filtering based estimation algorithm for a class of distributed hybrid systems. The hybrid estimation methodology is demonstrated on a cryogenic propulsion system.

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Design of Observers for Hybrid Systems

Cited by 220 publications

References 16 publications

Control of Hidden Mode Hybrid Systems: Algorithm termination

Control of Hidden Mode Hybrid Systems: Algorithm termination

Identification of PWARX hybrid models with unknown and possibly different orders

Estimation of Distributed Hybrid Systems Using Particle Filtering Methods

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