Query: event trigger multiagen discrete systems

title: Event trigger Control of Discrete Two time Scale System by Leveraging its Intrinsic Properties
abstract: There are several physical systems which exhibit two time scale property. When these systems are controlled through networks, uniform sampling causes an extra burden on the network bandwidth. Furthermore, uniform sampling of slow and fast variables leads to over sampling of slow variables. This work proposes an event trigger theory which leverages the physical property of discrete two time scale system to reduce the requirement of network bandwidth. Events in slow and fast variables occur as per their respective dynamics and based on the occurrence of a respective event, only slow or fast variables are communicated through a network. Thus the communication of the state is carried out based on an event of the respective set of variables. Simulation studies are presented to validate the theoretical developments.

title: Hyperplane design for discrete time sliding mode control with event trigger strategy and disturbance observer
abstract: In this study, a simple hyperplane design method based on the discrete time Riccati equation is proposed, and the inter relations are investigated for the basic requirement on sliding surface selection, the assumption of stabilisability, the Riccati inequality and the standard Riccati equation. A state and disturbance observer is embedded in discrete time sliding mode control (DSMC) to achieve its applicability when only partial system states can be measured. In order to improve the performance of the system in control updating times, a novel event triggered DSMC with a state and disturbance observer is proposed. It is shown that the proposed method achieves a quasi sliding mode with a small boundary layer. Simulation examples are presented to show the effectiveness and advantages of the proposed design schemes.

title: Codesign of Event Trigger and Feedback Policy in Robust Model Predictive Control
abstract: This paper is concerned with the robust event triggered model predictive control (MPC) of discrete time constrained linear systems subject to bounded additive disturbances. We make use of the interpolation technique to construct a feedback policy and tighten the original system constraint accordingly to fulfill robust constraint satisfaction. A dynamic event trigger that allows the controller to solve the optimization problem only at triggering time instants is developed, where the triggering threshold is related to the interpolating coefficient of the feedback policy and determined via optimization. We show that the proposed algorithm is recursively feasible and the closed loop system is input to state stable in the attraction region. Finally, a numerical example is provided to verify the theoretical results.

title: Fast data driven iterative event triggered control for nonlinear networked discrete systems with data dropouts and sensor saturation
abstract: The tracking control problem is investigated for a class of nonlinear networked discrete systems with random data dropout and sensor saturation, and a novel data driven iterative learning event triggered control scheme is proposed. First, a new model is established to describe random data dropout processes. Then, the fixed threshold iterative learning control scheme based on randomly received saturated output data is designed to track the desired trajectory and reduce the update number of iterations. Further, in order to obtain a faster convergence or learning speed, a novel method based on varying parameter along iteration axis is given. In the end, the resulting closed loop system is proved to be stable and the relationship between the upper bound of the consecutive data dropout number and system stability is revealed. Complete simulations are exploited to verify theoretical results.

title: Two channel event triggered model predictive control for discrete linear systems
abstract: This paper studies an event triggered model predictive control problem for constrained discrete time linear systems in the presence of bounded disturbances. For reducing the communication and computational loads, two different event triggered strategies are constructed in the sensor and the controller nodes, respectively. The first event trigger is introduced to schedule the transmission of signals from the sensor node to the local controller node; and the second event trigger is employed to determine the instant when the optimal control problem in the MPC based controller node should be solved. By constructing a feasible control sequence, the sufficient conditions are derived for guaranteeing the feasibility and stability of the closed loop systems. Finally, a simulation example is provided to illustrate the feasibility and the effectiveness of the proposed strategy.

title: Event Triggered Dynamic Output Feedback Control For Discrete Time Polytopic Linear Parameter Varying Systems
abstract: In this paper, we present a synthesis condition for designing event triggered dynamic output feedback control of discrete time polytopic linear parameter varying (LPV) systems. Here we assume that an event trigger controls the transmission of data from plant sensors to the controller in order to save communication bandwidth. The output feedback control scheme guarantees stability and a bound on the overall l2 performance. The synthesis problem is formulated as an LMI problem. The effectiveness of the result is illustrated in a simulated tracking scenario with a non holonomic vehicle modeled as a kinematic unicycle.

title: Dynamic Event Triggered Control for Discrete Time Markov Jump Systems
abstract: This paper concentrates on the dynamic event trigger based controller design for discrete time Markov jump systems. The rich data transmission within the network channel may cause the collision and the waste of resources, which leads to some kinds of poor system performance. Firstly, a discrete time domain dynamic event trigger mechanism is proposed for controller design. Secondly, for discrete time Markov jump systems, a mode dependent Lyapunov function is designed to ensure the system state to be exponentially mean square ultimately bounded by linear matrix inequalities (LMIs) Finally, a numerical simulation example is conducted to illustrate the effectiveness of the provided method.

title: Model Based Event Triggered Control of a Class of Strict Feedback Nonlinear Discrete Time Systems
abstract: In this paper, we present a novel model based event triggered adaptive neural network (NN) tracking control scheme for a class of strict feedback discrete time nonlinear systems. By using the event sampled NN to approximate the uncertainties of the system, an NN based adaptive model is first constructed and then the controller is designed by means of backstepping. In this scheme, the feedback state signals and NN weights signals are updated in an aperiodic manner at the triggering instants which is depending on the adaptive event trigger condition. Specially, the adaptive event trigger condition consists of the NN estimated weights and some design parameters to determine the trigger instants. The Lyapunov method and the decoupled backstepping technique is used to verify the ultimate boundedness of the NN weight error and the system signals. Simulation results are given to verify the effectiveness of the control scheme.

title: Event Triggered Fault Detection and Isolation of Discrete Time Systems Based on Geometric Technique
abstract: This brief considers the problem of fault detection and isolation using the geometric approach for discrete time systems. The description of unobservable subspace is presented. The residuals are generated by the filter via employing the geometric technique so that each residual is influenced by a specific fault and uncoupled from others. Sufficient conditions are established such that the \mathcal {H}}_{\infty norm of the transfer function is less than a pre given value. In order to reduce computational cost, an event triggered mechanism is presented to determine whether the current data should be released or not. Finally, numerical demonstration is given to verify the usefulness of the proposed new design technique.

title: Neural network based event triggered fault detection of discrete time nonlinear uncertain systems
abstract: This paper provides a novel neural network based event triggered fault detection (FD) strategy for nonlinear discrete time systems with unknown function, unmodeled dynamics and disturbances. An FD observer is proposed in the framework of neural network (NN) approximation and event triggered mechanism. Based on the stability analysis at the trigger instants and during the inter event time intervals, the aperiodic NN weight update law is proposed to remain ultimately bounded of the state and weight estimation errors. The FD decision logic is proposed follows by fault detectability analysis. The scheme ensures the reduction of communication burden while maintaining FD performance. Simulation results state the validity of the method.