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Finite-dimensional observer-based controller design for PDEs is a challenging problem. Recently, such controllers were introduced for the 1D heat equation, under the assumption that one of the observation or control operators is bounded. This paper suggests a constructive method for such controllers for 1D parabolic PDEs with both (observation and control) operators being unbounded. We consider the Kuramoto-Sivashinsky equation (KSE) under either boundary or in-domain point measurement and boundary actuation. We employ a modal decomposition approach via dynamic extension, using eigenfunctions of a Sturm-Liouville operator. The controller dimension is defined by the number of unstable modes, whereas the observer dimension $N$ may be larger than this number. We suggest a direct Lyapunov approach to the full-order closed-loop system, which results in an LMI whose elements and dimension depend on $N$. The value of $N$ and the decay rate are obtained from the LMI. We extend our approach to internal stabilization with guaranteed $L^2$-gain and input-to-state stabilization. We prove two crucial properties of the derived LMIs. First, We prove that the LMIs are always feasible provided $N$ and the $L^2$ or ISS gains are large enough, thereby obtaining guarantees for our approach. Moreover, for the case of stabilization, we show that feasibility of the LMI for some $N$ implies its feasibility for $N+1$ (i.e., enlarging $N$ in the LMI cannot deteriorate the resulting decay rate of the closed-loop system). Numerical examples demonstrate the efficiency of the method.

International audience; In this paper, input-to-state stability and stabilization conditions in time-delay generalized Persidskii systems are studied. These conditions are formulated in terms of linear matrix inequalities, which may depend on delay values, and be local or global in state space. Numerical examples of opinion dynamics and the Lotka-Volterra model illustrate the efficiency of the proposed results.

This paper addresses sampled-data control of 2D Kuramoto-Sivashinsky equation over a rectangular domain. We suggest to divide the 2D rectangular into N sub-domains, where sensors provide spatially averaged or point state measurements to be transmitted through communication network to the controller. Note that differently from 2D heat equation, here we manage with sampled-data control under point measurements. We design a regionally stabilizing controller applied through distributed in space characteristic functions. Sufficient conditions ensuring regional stability of the closed-loop system are established in terms of linear matrix inequalities (LMIs). By solving these LMIs, we find an estimate on the set of initial conditions starting from which the state trajectories of the system are exponentially converging to zero. A numerical example demonstrates the efficiency of the results.

We develop a PDE-based approach to multi-agent deployment where each agent measures its relative position to only one neighbor. First, we show that such systems can be modeled by a first-order hyperbolic partial differential equation (PDE) whose L2-stability implies the stability of the multi-agent system for a large enough number of agents. Then, we show that PDE modelling helps to construct a Lyapunov function for the multi-agent system using spatial discretisation. Then, we use the PDE model to estimate the leader input delay preserving the stability.

In this paper, we study the digital implementation of derivative-dependent control for consensus of stochastic multi-agent systems. The consensus controllers that depend on the output and its derivatives are approximated as delayed sampled-data controllers. First, we consider the nth-order stochastic multi-agent systems. Second, we consider PID control of the second-order stochastic multi-agent systems. For the consensus analysis, we propose novel Lyapunov-Krasovskii functionals to derive linear matrix inequalities (LMIs) that allow to find admissible sampling period. The efficiency of the presented approach is illustrated by numerical examples.

In the present paper, we consider network-based control of linear systems with state multiplicative noise. For the sensor-controller network, Round-Robin (RR), try-once-discard (TOD) and independent and identically-distributed (i.i.d) protocols are proposed to orchestrate the measurement transmission from multiple sensor nodes. By using the time-delay approach to networked control systems, sufficient conditions for the exponential mean-square stabilization are obtained in terms of linear matrix inequalities (LMIs). Moreover, a discrete-time dynamic event-trigger is suggested to reduce the number of sent control signals from controller to actuator. An air vehicle system from the literature illustrates the efficiency of the presented approach.