This work presents a novel masking protocol to secure the communication between a nonlinear plant and a non-linear observer. Communication is secured in two senses. First, the privacy of the plant is preserved during the communication. Second, the protocol can detect a false-data injection attack in the communication link. The masking protocol is based on the use of washout-filters in nonlinear observers and the internal model principle.

A unifying design perspective is presented for emulation-based (dynamic) event-triggered state-feedback control of nonlinear systems. The main component of this new approach is to interpret event-triggered controlled systems as the interconnection of hybrid dynamical systems and to analyze the overall system using a hybrid small gain theorem. Based on this new perspective, we unify several event-triggered schemes that were previously proposed in the literature under one umbrella. Moreover, the design approach offers great flexibility and can be used for the development of novel event-triggered schemes and systematic modification and improvement of existing triggering strategies. In this article, we illustrate via simulations that these novel and/or modified event-triggered controllers can lead to a further reduction in the required number of transmissions, while still guaranteeing stability.

Progress towards effective treatment of epileptic seizures has seen much improvement in the past decade. In particular, the emergence of phenomenological models of epileptic seizures specifically designed to capture the electrical seizure dynamics in the Epileptor model is inspiring new approaches to predicting and controlling seizures. These new models present in various forms and contain important but unmeasurable variables that control the occurrence of seizures. These models have been used mostly as nodes in large networks to study the complex brain behaviour of seizures. In order to use this model for the purposes of seizure forecasting or to control seizures through deep brain stimulation, the states of the model will need to be estimated. Although devices such as EEG electrodes can be related to some of the states of the model, most remain unmeasured and would require an observer (as defined in control theory) for their estimation. Additionally, we would like to consider the case for large nodes of systems where the number of electrodes is far smaller than the number of nodes being estimated. In this paper, we provide methods towards obtaining the full states of these phenomenological models using nonlinear observers. In particular, we explore the effectiveness of the Extended Kalman Filter for small networks of nodes of a smoothed sixth order Epileptor model. We show that observer design is possible for this family of systems and identify the difficulties in doing so.Clinical relevance—The methods presented here can be applied with an individual epileptic patient’s EEG to reveal previously hidden biomarkers of epilepsy for seizure forecasting.

By sharing local sensor information via Vehicle-to-Vehicle (V2V) wireless communication networks, Cooperative Adaptive Cruise Control (CACC) is a technology that enables Connected and Automated Vehicles (CAVs) to drive autonomously on the highway in closely-coupled platoons. The use of CACC technologies increases safety and the traffic throughput, and decreases fuel consumption and CO2 emissions. However, CAVs heavily rely on embedded software, hardware, and communication networks that make them vulnerable to a range of cyberattacks. Cyberattacks to a particular CAV compromise the entire platoon as CACC schemes propagate corrupted data to neighboring vehicles potentially leading to traffic delays and collisions. Physics-based monitors can be used to detect the presence of False Data Injection (FDI) attacks to CAV sensors; however, unavoidable system disturbances and modelling uncertainty often translates to conservative detection results. Given enough system knowledge, adversaries are still able to launch a range of attacks that can surpass the detection scheme by hiding within the system disturbances and uncertainty -- we refer to this class of attacks as \textit{stealthy FDI attacks}. Stealthy attacks are hard to deal with as they affect the platoon dynamics without being noticed. In this manuscript, we propose a co-design methodology (built around a series convex programs) to synthesize distributed attack monitors and $H_{\infty}$ CACC controllers that minimize the joint effect of stealthy FDI attacks and system disturbances on the platoon dynamics while guaranteeing a prescribed platooning performance (in terms of tracking and string stability). Computer simulations are provided to illustrate the performance of out tools.

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 multiobserver 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.

We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the system are unknown. To address this challenge, we propose a certainty-equivalent control scheme combining online parameter estimation with saturated linear control. We establish the existence of a high probability stability bound on the closed-loop system, under additional assumptions on the system and noise processes. Numerical examples are presented to illustrate our results.

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 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 battery model).

We study nonlinear networked control systems (NCS), where the controller is implemented over multiple processors via an emulation-based approach. We start with a stable and centralised NCS commonly considered in the literature. Then, we show how to implement the centralised controller over multiple processors inspired by parallel computing techniques, so that stability is preserved (semi-globally and practically) under sufficiently fast computations. An example is given to illustrate the main results.

Transmit power control is one of the most important issues in wireless networks, where nodes typically operate on limited battery power. Reducing communicating power consumption is essential for both economic and ecologic reasons. In fact, transmitting at unnecessarily high power not only reduces node lifetime, but also introduces excessive interference and electromagnetic pollution. Existing work in the wireless community mostly focus on designing transmit power policies by taking into account communication aspects like quality of service or network capacity. Wireless networked control systems (WNCSs), on the other hand, have different and specific needs such as stability, which require transmit power policies adapted to the control context. Transmit power design in the control community has recently attracted much attention, and available works mostly consider linear systems or specific classes of non-linear systems with a single-link view of the system. In this paper, we propose a framework for the design of stabilising transmit power levels that applies to much larger classes of non-linear plants, controllers, and multi-link setting. By exploiting the fact that channel success probabilities are related to transmit power in a non-linear fashion, we first derive closed-loop stability conditions that relate channel probabilities with transmission rate. Next, we combine these results together with well-known and realistic interference models to provide a design methodology for stabilising transmit power in non-linear and multi-link WNCSs.

We present a new algorithm called policy iteration plus (PI+) for the optimal control of nonlinear deterministic discrete-time plants with general cost functions. PI+ builds upon classical policy iteration and has the distinctive feature to enforce recursive feasibility under mild conditions, in the sense that the minimization problems solved at each iteration are guaranteed to admit a solution. While recursive feasibility is a desired property, it appears that existing results on the policy iteration algorithm fail to ensure it in general, contrary to PI+. We also establish the recursive stability of PI+: the policies generated at each iteration ensure a stability property for the closed-loop system. We prove our results under more general conditions than those currently available for policy iteration, by notably covering set stability. Finally, we present characterizations of near-optimality bounds for PI+ and prove the uniform convergence of the value functions generated by PI+ to the optimal value function. We believe that these results would benefit the burgeoning literature on approximate dynamic programming and reinforcement learning, where recursive feasibility is typically assumed without a clear method for verifying it and where recursive stability is essential for safe operation of the system.

This paper investigates recursive feasibility, recursive robust stability and near-optimality properties of policy iteration (PI). For this purpose, we consider deterministic nonlinear discrete-time systems whose inputs are generated by PI for undiscounted cost functions. We first assume that PI is recursively feasible, in the sense that the optimization problems solved at each iteration admit a solution. In this case, we provide novel conditions to establish recursive robust stability properties for a general attractor, meaning that the policies generated at each iteration ensure a robust \KL-stability property with respect to a general state measure. We then derive novel explicit bounds on the mismatch between the (suboptimal) value function returned by PI at each iteration and the optimal one. Afterwards, motivated by a counter-example that shows that PI may fail to be recursively feasible, we modify PI so that recursive feasibility is guaranteed a priori under mild conditions. This modified algorithm, called PI+, is shown to preserve the recursive robust stability when the attractor is compact. Additionally, PI+ enjoys the same near-optimality properties as its PI counterpart under the same assumptions. Therefore, PI+ is an attractive tool for generating near-optimal stabilizing control of deterministic discrete-time nonlinear systems.

We present stability conditions for deterministic time-varying nonlinear discrete-time systems whose inputs aim to minimize an infinite-horizon time-dependent cost. Global asymptotic and exponential stability properties for general attractors are established. This work covers and generalizes the related results on discounted optimal control problems to more general systems and cost functions.

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.

Various methods are nowadays available to design observers for broad classes of systems. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. This paper presents a general supervisory design framework for online tuning of the observer gains with the aim of achieving various trade-offs between robustness and speed of convergence. We assume that a robust nominal observer has been designed for a general nonlinear system and the goal is to improve its performance. We present for this purpose a novel hybrid multi-observer, which consists of the nominal one and a bank of additional observer-like systems, that are collectively referred to as modes and that differ from the nominal observer only in their output injection gains. We then evaluate on-line the estimation cost of each mode of the multi-observer and, based on these costs, we select one of them at each time instant. Two different strategies are proposed. In the first one, initial conditions of the modes are reset each time the algorithm switches between different modes. In the second one, the initial conditions are not reset. We prove a convergence property for the hybrid estimation scheme and we illustrate the efficiency of the approach in improving the performance of a given nominal high-gain observer on a numerical example.