Nacim Ramdani

A framework for set-based state estimation in multi-sensor cyber-physical systems with delayed measurements is presented, integrating defense-in-depth strategies while ensuring optimal system resource utilization. An interval impulsive observer is designed, combining a moving target defense strategy with self-triggered sampling. The finite-gain L1 stability of the estimation errors is analyzed using hybrid system tools. By leveraging positive realization and interval analysis, sufficient conditions for synthesis are formulated as an optimization problem under algebraic inequalities. The approach is illustrated through a simple example.

This paper presents a recursive ellipsoidal set-membership state estimation algorithm for discrete-time linear time- varying (LTV) models with additive bounded disturbances affecting state evolution and sporadic measurement equations. The state vector is subject to linear equality and/or inequality constraints, which are mathematically viewed as additional measurements. A novel approach is developed considering the unprecedented fact that, owing to equality constraints, the ellipsoid characterizing all possible values of the state vector has a zero volume and its shape matrix is non invertible. A new size criterion, the pseudo-volume, is introduced and minimized in both the prediction and correction phases.

This paper focuses on a day-ahead scheduling problem on generating power of thermal power plants and charge/discharge power of multiple storage batteries based on the information of the interval prediction of photovoltaic (PV) /demand power. Our purpose is to obtain an exact range of each optimal operation schedule for any possible PV/demand profiles of the interval prediction. To find this range efficiently, it is necessary to focus on the monotonicity of the solution with respect to the parameter, which corresponds to the PV/demand. If the solution is monotonically increasing or decreasing with respect to the parameter, we can find the exact range of the solution in a finite number of trials. The monotonicity has been analyzed in various power settings, and the exact range of the optimal operation schedule has been calculated so far. However, we could not treat multiple storage batteries in the previous work. To adapt to future power system problems, it is necessary to analyze the monotonicity in the case where the supplier has multiple storage batteries. In this paper, we show sufficient conditions for the monotonicity of the optimal generating power for a thermal power plant taking into account existence of two storage batteries.

This work introduces a new approach based on an event‐triggered mechanism (ETM) to design finite‐gaininterval observers for linear continuous‐time systems in the presence of unknown‐but‐bounded uncertainties with a priori known bounds on state disturbances and measurement noises. In this setting, aperiodic measurements sampling is controlled in a way to reduce online communication between the sensors and the estimation algorithm. The proposed ETM relies on a dynamic condition that depends on the width of the feasible domain of the system's uncertainties and the width of the estimated state enclosures. Moreover, the proposed approach guarantees the existence of a positive lower bound on the interevent times, which avoids the Zeno phenomenon. On the other hand, although the sensors data are used in an irregular sampling way, the‐stability performance of the estimation error is satisfied. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

This article addresses the modeling and the design of an interval state observer for a linear time-invariant plant in the presence of sporadically available measurements corrupted by unknown-but-bounded errors and noise. The interval observer is modeled as an impulsive system where an impulsive correction is made whenever a measurement is available. The non-negativity of the observation error between two successive measurements is preserved by applying the internal positivity based on the Müller's existence theorem, while at measurement times a linear programming constraint is added. A new methodology for designing the discrete-time observer gain is proposed that guarantees both nonnegativity and stability of the estimation error. The synthesis is performed by solving a set of bilinear matrix inequalities (BMIs). The theoretical result is supported by numerical simulation.

This study proposes a robust tracking controller for an underactuated quadrotor model based on the flatness theory and active disturbance rejection control (ADRC). Exploiting the differential flatness characteristic, it is demonstrated that a non‐linear quadrotor system can be changed into a linear canonical form, where it is easier to create a state feedback controller that ensures accurate trajectory tracking. In order to improve the tracking performance of the quadrotor, other factors are considered in the conception of the state feedback controller consisting in the estimation of the un‐measurable state variables in a canonical (Brunovsky) form and in the elimination of external perturbations and modelling uncertainties affecting the quadrotor system. To deal with this problem, first, an extended state observer (ESO) is designed to estimate a system state and an extended state known as lumped uncertainties. The latter represents the total effects of uncertain parameters, the neglected part of non‐linearity and the external disturbances. Second, based on the ESO result, an additional term is integrated into the feedback controller to eliminate the lumped disturbance effects and to ensure the stability of the closed loop. Simulation results are introduced to prove the benefits of associating the ADRC approach with flatness control.

This paper presents simple design methods of interval observers for discrete-time linear switched systems in the presence of output noise and system perturbation. The interval observers are designed using the internal positivity of the system without need to a coordinate transformation. This approach is proposed for arbitrary switching signals and it can be generalized to dwell-time switching rules. The observation gain that guarantees both stability and positivity of the interval estimate errors is synthesized by solving a Linear Matrix Inequality (LMI) feasibility problem. The theoretical result is supported by numerical simulations.