Hybrid multi-observer for improving estimation performance

Various methods are nowadays available to design observers for broad classes of systems, where the primary focus is on establishing the convergence of the estimated states. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. In this context, we present a general design framework for the online tuning of the observer gains. Our starting point is a robust nominal observer designed for a general nonlinear system, for which an input-to-state stability property can be established. Our goal is then to improve the performance of this nominal observer. We present for this purpose a new hybrid multi-observer scheme, whose great flexibility can be exploited to enforce various desirable properties, e.g., fast convergence and good sensitivity to measurement noise. We prove that an input-to-state stability property also holds for the proposed scheme and, importantly, we ensure that the estimation performance in terms of a quadratic cost is (strictly) improved. We illustrate the efficiency of the approach in improving the performance of given nominal observers in two numerical examples (Van der Pol oscillator and Lithium-Ion (Li-Ion) battery model).Comment: arXiv admin note: text overlap with arXiv:2209.1013

State estimation of an electrochemical lithium-ion battery model: improved observer performance by hybrid redesign

Effective management and just-in-time maintenance of lithium-ion batteries require the knowledge of unmeasured (internal) variables that need to be estimated. Observers are thus designed for this purpose using a mathematical model of the battery internal dynamics. It appears that it is often difficult to tune the observers to obtain good estimation performances both in terms of convergence speed and accuracy, while these are essential in practice. In this context, we demonstrate how a recently developed hybrid multi-observer can be used to improve the performance of a given observer designed for an electrochemical model of a lihium-ion battery. Simulation results, obtained with standard parameters values, show the estimation performance improvement using the proposed method

Decentralized event-triggered estimation of nonlinear systems

We investigate the scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. The sensors are grouped into N nodes and each of these nodes decides when its measured data is transmitted over the network independently. The objective is to design both the observer and the local transmission policies in order to obtain accurate state estimates, while only sporadically using the communication network. In particular, given a general nonlinear observer designed in continuous-time satisfying an input-to-state stability property, we explain how to systematically design a dynamic event-triggering rule for each sensor node that avoids the use of a copy of the observer, thereby keeping local calculation simple. We prove the practical convergence property of the estimation error to the origin and we show that there exists a uniform strictly positive minimum inter-event time for each local triggering rule under mild conditions on the plant. The efficiency of the proposed techniques is illustrated on a numerical case study of a flexible robotic arm

A Framework for the Observer Design for Networked Control Systems

C1 - Journal Articles Referee

Inter-event Times Analysis for Planar Linear Event-triggered Controlled Systems

We analyse the properties of the inter-event times for planar linear time-invariant systems controlled by an event-triggered state-feedback law. The triggering rule is given by the relative threshold strategy and we assume that the tunable triggering parameter is small. Several cases are distinguished depending on the nature of the eigenvalues of the (continuous-time) closed-loop system matrix in absence of sampling. When these eigenvalues are real, it is shown that the inter-event times lie in a neighborhood of a given constant for all positive times or converge to the neighborhood of a given constant as time grows. When the eigenvalues are complex conjugates, the inter-event times oscillate with a varying period for which we give an estimate. Moreover, the values taken by the inter-event times over this varying period are approximately the same for all initial conditions. As a consequence, one can run a single simulation over a given interval of time to infer properties of the inter-event times for all initial conditions and all positive times. Numerical simulations are provided to support the presented theoretical guarantees. These results help to understand the behaviour of the inter-event times, instead of solely relying on numerical simulations, and can be exploited to evaluate the performance of the considered triggering condition in terms of average inter-transmission times

Event-Triggered Control in Presence of Measurement Noise: A Space-Regularization Approach

In this paper, general conditions for set stabilization of (distributed) event-triggered control systems affected by measurement noises are presented. It is shown that, under these conditions, both static and dynamic triggers can be designed using a space-regularization approach such that the closed-loop system ensures an input-to-state practical stability property. Additionally, by proper choice of the tuning parameters, the system does not exhibit Zeno behavior. Contrary to various results in the literature, the noises do not have to be differentiable. The general results are applied to point stabilization and consensus problems as particular cases. Simulations illustrate our results

Event-Triggered State Estimation with Multiple Noisy Sensor Nodes

General nonlinear continuous-time systems are considered for which its state is estimated via a packet-based communication network. We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit at discrete (non-equidistant) points in time. Moreover, each node can transmit asynchronously. For this setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in either time-triggered or event-triggered fashions. In the latter case, we guarantee the absence of Zeno behavior by construction. It is shown that, under the provided design conditions, an input-to-state stability property is obtained for the estimation error with respect to the measurement noise and process disturbances and that the state is thus reconstructed asymptotically in the absence of noise