Abstract-In this paper, event-triggered strategies for control of discrete-time systems are proposed and analyzed. Similarly to the continuous-time case, the plant is assumed input-tostate stable with respect to measurement errors and the control law is updated once a triggering condition involving the norm of a measurement error is violated. The results are also extended to a self-triggered formulation, where the next control updates are decided at the previous ones, thus relaxing the need for continuous monitoring of the measurement error. The overall framework is then used in a novel Model Predictive Control approach. The results are illustrated through simulated examples.

Abstract-This paper proposes novel event-triggered strategies for the control of uncertain nonlinear systems with additive disturbances under robust Nonlinear Model Predictive Controllers (NMPC). The main idea behind the event-driven framework is to trigger the solution of the optimal control problem of the NMPC, only when it is needed. The updates of the control law depend on the error of the actual and the predicted trajectory of the system. Sufficient conditions for triggering are provided for both, continuous and discretetime nonlinear systems. The closed-loop system evolves to a compact set where it is ultimately bounded, under the proposed framework. The results are illustrated through a simulated example.

In this paper, novel event-triggered strategies for the design of model predictive (MPC) controllers are presented. The MPC framework consists in finding the solution to a constraint optimal-control problem at every time-step. The case of triggering the optimization of the MPC only when is needed, is investigated. The centralized case is treated first and the results are then extended to a decentralized formulation. We consider a system composed by a number of interconnected subsystems, each one of them controlled by a robust MPC algorithm. Using the Input-to-State (ISS) property of the decentralized MPC controller we reach to an event-triggering rule, for each of the subsystems.

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This paper presents a novel Vision-based Nonlinear Model Predictive Control (NMPC) scheme for an underactuated underwater robotic vehicle. In this scheme, the control loop does not close periodically, but instead a self-triggering framework decides when to provide the next control update. Between two consecutive triggering instants, the control sequence computed by the NMPC is applied to the system in an openloop fashion, i.e, no state measurements are required during that period. This results to a significant smaller number of requested measurements from the vision system, as well as less frequent computations of the control law, reducing in that way the processing time and the energy consumption. The image constraints (i.e preserving the target inside the camera's field of view), the external disturbances induced by currents and waves, as well as the vehicle's kinematic constraints due to under-actuation, are being considered during the control design. The closed-loop system has analytically guaranteed stability and convergence properties, while the performance of the proposed control scheme is experimentally verified using a small underactuated underwater vehicle in a test tank.