Co-design of output feedback laws and event-triggering conditions for linear systems

We present a procedure to simultaneously design the output feedback law and the event-triggering condition to stabilize linear systems. The closed-loop system is shown to satisfy a global asymptotic stability property and the existence of a strictly positive minimum amount of time between two transmissions is guaranteed. The event-triggered controller is obtained by solving linear matrix inequalities (LMIs). We then exploit the flexibility of the method to maximize the guaranteed minimum amount of time between two transmissions. Finally, we provide a (heuristic) method to reduce the amount of transmissions, which is supported by numerical simulations

Triggering mechanism using freely selected sensors for linear time-invariant systems

International audienceExisting event-triggering sampling techniques for state feedback controllers generally use full state information. This may be a source of important computational and communication costs, particularly if sensors are numerous and/or distributed, as the triggering condition needs to be continuously evaluated. We propose an approach to redesign triggering mechanisms for linear time-invariant systems based on limited sensors information, which we freely select, and an internal scalar dynamic variable. We prove that the resulting closed loop system ensures a uniform global exponential stability property and that a uniform minimal inter-execution time is guaranteed. Besides stabilizability, no additional assumption on the system is needed. Guidelines are provided to derive the redesigned triggering conditions using linear matrix inequalities. We show on an academic example that, using a suitable choice of sensors and parameters, inter-execution times using our approach are comparable to those using triggering mechanisms that monitor the full state of the syste

Output feedback stabilization for uncertain nonlinear time-delay systems subject to input constraints

International audienceRobust stabilization of a class of imperfectly known systems with time-varying time-delays via output feedback is investigated. The systems addressed are composed of a nonlinear nominal sys- tem influenced by nonlinear perturbations which may be time-, state, delayed state, and/or input- dependent. The output of the system is modelled by a nonlinear function, which may depend on the delayed states, and inputs, together with a feed-through term. Using bounding information on the perturbations, in terms of specified growth conditions, classes of unconstrained and constrained output feedback controllers are designed in order to guarantee a prescribed stability property for the closed-loop systems, provided appropriate stability criteria hold. Two stability criterion are given: one in terms of a Linear Matrix Inequality (LMI), the other is algebraic in nature, obtained using a Gersgorin theorem

Supervisory observer for parameter and state estimation of nonlinear systems using the DIRECT algorithm

A supervisory observer is a multiple-model architecture, which estimates the parameters and the states of nonlinear systems. It consists of a bank of state observers, where each observer is designed for some nominal parameter values sampled in a known parameter set. A selection criterion is used to select a single observer at each time instant, which provides its state estimate and parameter value. The sampling of the parameter set plays a crucial role in this approach. Existing works require a sufficiently large number of parameter samples, but no explicit lower bound on this number is provided. The aim of this work is to overcome this limitation by sampling the parameter set automatically using an iterative global optimisation method, called DIviding RECTangles (DIRECT). Using this sampling policy, we start with 1 + 2np parameter samples where np is the dimension of the parameter set. Then, the algorithm iteratively adds samples to improve its estimation accuracy. Convergence guarantees are provided under the same assumptions as in previous works, which include a persistency of excitation condition. The efficacy of the supervisory observer with the DIRECT sampling policy is illustrated on a model of neural populations

Observer design for networked control systems with FlexRay

International audienceWe design state observers for nonlinear networked control systems (NCS) implemented over FlexRay. FlexRay is a communication protocol used in the automotive industry, which has the feature to switch between two scheduling rules during its communication cycles. These switches induce technical difficulties when modeling, designing and analysing observers for such systems compared to standard NCS. We present a solution based on the emulation approach. Given an observer in the absence of communication constraints, we implement it over the network and we provide sufficient conditions on the latter, to preserve the stability property of the observer. In particular, we provide explicit bounds on the maximal allowable transmission intervals , which adapt to the lengths of the segment associated to each scheduling rule. We assume that the plant dynamics and measurements are affected by noise and we guarantee an input-to-state stability property for the corresponding estimation error system. The overall system is modeled as a hybrid system and the analysis relies on the use of a novel hybrid Lyapunov function

Enhancing accuracy of finite-dimensional models for lithium-ion batteries, observer design and experimental validation

Accurate estimation of the internal states of lithium-ion batteries is key towards improving their management for safety, efficiency and longevity purposes. Various approaches exist in the literature in this context, among which designing an observer based on an electrochemical model of the battery dynamics. With this approach, the performance of the observer depends on the accuracy of the considered model. It appears that electrochemical models, and thus their associated observer, typically require to be of high dimension to generate accurate internal variables. In this work, we present a method to mitigate this limitation by correcting the lithium concentrations generated by a general class of finite-dimensional electrochemical models such that they asymptotically match those generated by the original partial differential equations (PDE) they are based on, for constant input currents. These corrections apply irrespectively of the order of the considered finite-dimensional model. The proposed correction leads to a new state space model for which we design observers, whose global, robust convergences are supported by a Lyapunov analysis. Both numerical and experimental validations are presented, which show the improvement of the accuracy of the state estimates as a result of the proposed corrections

Uniting observers

International audienceWe propose a framework for designing observers possessing global convergence properties and desired asymptotic behaviours for the state estimation of nonlinear systems. The proposed scheme consists in combining two given continuous-time observers: one, denoted as global, ensures (approximate) convergence of the estimation error for any initial condition ranging in some prescribed set, while the other, denoted as local, guarantees a desired local behaviour. We make assumptions on the properties of these two observers, and not on their structures, and then explain how to unite them as a single scheme using hybrid techniques. Two case studies are provided to demonstrate the applicability of the framework. Finally, a numerical example is presented