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Event-triggered control, as networked systems are popular, is increasingly appreciated. Such control could arouse essential saving of communication/computation and would lay the groundwork for networked (control) systems. However, due to the discontinuity and hybrid nature of event-triggered control, the exclusion of Zeno phenomenon is the premise of its workability. Aiming to circumvent the Zeno behaviour more explicitly, we attach a dynamic dwell time to triggering mechanism and propose a new adaptive event-triggered control scheme for uncertain nonlinear systems. Specifically, a set of dynamic gains are first introduced to compensate for large uncertainties and nonlinearities, and the indispensability of a set of gains rather than a gain is elaborated on. Then, from a dynamic-gain-dependent monitoring rule, the positive dwell time is generated so that the Zeno phenomenon can be excluded automatically. In particular, the rule enables system behaviour to be always monitored by evaluating the sampling errors of gains. Based on this, we construct an adaptive controller with linear structure, which allows the execution error can be estimated explicitly, and in turn suppressed by the gains. It is shown that, with the proposed event-triggered control, all the closed-loop system signals are bounded and the system state ultimately converges to zero. Notably, the designed triggering mechanism could save more resources in theory since the event detection and gains monitoring are performed alternately. The effectiveness and superiority of the proposed scheme is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

We study in this work the mixed -filtering problem for Markov jump linear systems in a partial observation context. Rather than the Markov chain , we consider that only an estimation coming from a detector is available to the filter. We present a sufficient condition for the synthesis of a filter depending only on such that the estimation errors for the and filtering problems are bounded by given scalars. The robust mixed filtering considering that the transition and detection probabilities are uncertain, as well as the complete observation, cluster and mode-independent cases, are also studied. The results are given in terms of linear matrix inequalities and are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model. [ABSTRACT FROM AUTHOR]

High-precision entry navigation capability is essential for future Mars pinpoint landing missions. An augmented robust three-stage extended Kalman filter (ARThSEKF) for integrated navigation algorithm of Mars atmospheric entry with models containing parameter uncertainties and measurement errors is presented in this paper. The derivation is conducted, and the character of stability has also been analysed, in which it has been proved to be uniformly asymptotically stable. In the further simulation of Mars entry-phase navigation, ARThSEKF showed a good performance to compare with the standard extended Kalman filter. As the atmosphere density uncertainties and unknown measurement errors have been estimated precisely, the state estimation errors were controlled to a low level, of which the position and velocity were less than 100 m and 5 m/s, respectively. Therefore, ARThSEKF is suitable for dealing with non-linear systems in the presence of parameter uncertainties and unknown measurement errors, which can fulfil the requirement of future pinpoint Mars landing mission. [ABSTRACT FROM PUBLISHER]

This paper presents a systematic routine for jointly reconstruct the state variables and the unknown inputs (UIs) for a large class of nonlinear MIMO systems. After an appropriate change of state coordinates, a set of cascade high-gain observers (CHGO) are designed in such a way that each of them provides an estimation of only one component of the UI vector, except the last one which gives a final adjustment of the whole state variables. Such design achieves a boundary of the state estimation error which can be arbitrarily small by properly specifying the sole synthesis parameter. An illustrative example verifies the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Security issues of cyber-physical systems have received increasing attentions in recent years. In this paper, deception attacks on the remote state estimator equipped with the chi-squared failure detector are considered, and it is assumed that the attacker can monitor and modify all the sensor data. A novel adaptive optimisation-offline cyber attack strategy is proposed, where using the current and previous sensor data, the attack can yield the largest estimation error covariance while ensuring to be undetected by the chisquared monitor. From the attacker's perspective, the attack is better than the existing linear deception attacks to degrade the systemperformance. Finally, some numerical examples are provided to demonstrate theoretical results. [ABSTRACT FROM AUTHOR]

The paper considers the issues of state estimation and output disturbance reconstruction for a class of switched linear systems with unknown inputs. A singular switched system is derived from the original switched system by taking the output disturbance as a part of a new extended state vector. For the constructed singular switched system, a robust sliding-mode switched observer which can not only estimate the states of original switched system but also reconstruct output disturbances is proposed, where the switching of the observer is synchronous with that of the switched system. A sufficient condition is provided to guarantee the existence of the switched observer by the feasibility of an optimisation problem with linear matrix inequality constraint, and the corresponding switching signal with average dwell time is designed such that the convergence of the estimation error system is proven to be exponential. Based on the state estimation of singular switched system, the methods of state estimation and output disturbance reconstruction of original switched system are proposed. Finally, the simulation results confirm the predicted performance of the proposed methods. [ABSTRACT FROM AUTHOR]

In a distributed estimation system, the local sensor estimates are transferred to fusion center and fused to be an optimal estimation according to some criterion. The well-known best linear unbiased estimation (BLUE) fusion optimally combines the local estimates by minimising the covariance matrix of estimation errors. However, the BLUE fusion need know the cross-covariances of sensor estimation errors exactly, which are not attainable in most of practical applications. In this article, the random matrix is employed to describe the uncertainty of the estimation error covariance among the sensors. By minimising the estimation error covariance only for the most favorable realisations of the random matrix, we model it as an optimisation problem with chance constraint. With appropriate relaxation method, a robust linear unbiased estimation fusion is proposed analytically. Furthermore, an upper bound on the relaxation gap is proposed. Finally, we demonstrate through some numerical simulations that the presented estimation fusion always has smaller absolute estimation error than that of the covariance intersection filter. [ABSTRACT FROM AUTHOR]