Switched sliding mode control strategy for networked systems

In this paper, a networked switched control strategy based on Sliding Mode Control is presented. The idea pursued in this work is to reduce to a minimum the packet rate over the network, in order to limit the problems induced by the transmission of the state measurement between the sensor and the controller, while providing performance comparable with that of a non networked Sliding Mode Control scheme. The proposed scheme includes a model based controller which contains the nominal model of the plant, and relies on a suitably defined triggering condition. The latter considers the amplitude of a sliding variable determined relying on nominal model, and enables the actual state transmission only when the sliding variable is within a predefined boundary layer. When the plant state is not transmitted, the model state is used to determine the control action. In this way, it is possible to guarantee the same robustness with respect to matched uncertainties as in conventional sliding mode control schemes, as well as the exponential stability of the origin of the controlled system state space, even if the actual system state is not always used to close the feedback. Moreover, in steady-state, when the boundary layer is reached, in order to avoid a continuous transmission of the actual state measurement, a mechanism based on a moving average of the current sliding variable is adopted, which allows to suitably deactivate the state transmission even within the boundary layer, yet maintaining some robustness. Simulation results demonstrates the effectiveness of the proposed strategy

Model-based event-triggered robust MPC/ISM

A model-based event-triggered control scheme based on the combined use of Model Predictive Control (MPC) and Integral Sliding Mode (ISM) control is proposed in this paper. The aim is to reduce to a minimum the number of transmissions of the plant state over the network, in order to alleviate delays and packet loss induced by the network overload, while guaranteeing robust stability and constraints fulfillment. The presented control scheme includes a model-based controller and a smart sensor, both containing a copy of the nominal model of the plant. The sensor intelligence is provided by a triggering condition, which enables to determine when it is necessary to transmit the measured state and to update the nominal model. The controller includes an ISM component, which has the role of compensating the uncertainties, and a MPC term which optimizes the system evolution. The control system performance are assessed in simulation relying on an illustrative mechanical example

Construction of event-based ISS controllers on coarse quantizations

International audienceWe consider the construction of event-based input-to-state stabilizing state feedback controllers for perturbed nonlinear discrete time systems. The controllers are designed to be constant on possibly coarse quantization regions. An event is triggered upon every transition of the state from one quantization region to another. The practical contribution of the paper is an algorithmic design approach based on game theoretic ideas, feasible for low dimensional systems. The theoretical contribution consists of a novel piecewise constant event-based ISS Lyapunov function concept which is consistent with the imposed quantization

Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics

This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2