Non-uniform Multi-rate Estimator based Periodic Event-Triggered Control for resource saving

Abstract

This paper proposes a systematic non-uniform multi-rate estimation and control framework for a periodic event-triggered system which is subject to external disturbance and sensor noise. When the disturbance dynamic model is available, and in order to efficiently estimate the state variable and disturbance from non-uniform slow-rate measurements, a time-varying Kalman filter is designed. When the disturbance dynamic model is not available, a disturbance observer is proposed as an alternative approach. Both the Kalman filter and the disturbance observer are proposed in a non-uniform multi-rate format. Such disturbance estimation enables faster controller updating in spite of slower measurement. Interlacing techniques are used in the control system to uniformly distribute the computational load at each fast sampling instance. Compared to the conventional time-triggered sampling paradigm, the control solution is able to reduce the resource utilization, while maintaining a satisfactory control performance. The proposed control solution will reduce the number of transmissions among devices, which enhances the energy and computational efficiency. Simulation results are provided to validate the effectiveness and benefits of the proposed control algorithms.

Energy-Efficient Control for an Unmanned Ground Vehicle in a Wireless Sensor Network

“…In our work, dual-rate control and periodic event-triggered control (PETC) are integrated in order to save system resources (energy and bandwidth). As a consequence of using PETC, not all the measurements are sent to the base station, which results in a time-varying dual-rate Kalman filter (TVDRKF) [24][25][26]. From Extended State Observer (ESO) [46] and multirate Kalman filter [45,47] techniques, the proposed TVDRKF is able to estimate the plant measurement and disturbance, following a nonuniform dual-rate scheme.…”

“…Regarding event-based state prediction techniques, different approaches address this aspect in the literature, for instance, distributed event-triggered filtering in [13,14,22], a model-based predictor in [18,23], and time-varying Kalman filters in [24][25][26]. Following this line of research on time-varying Kalman filters, in this paper, a time-varying dual-rate Kalman filter (TVDRKF) is used in order to provide nonavailable data and compute an h-step ahead state prediction stage.…”

“…The nature of this filter is (i) dual-rate, due to the consideration of a slowrate state correction stage along with a fast-rate prediction stage, and (ii) time-varying, because the correction stage is carried out in a nonuniform fashion as a consequence of the event-triggered sampling scheme. Unlike [26], the Kalman filter is now integrated in a WSN, where communication problems such as network-induced delays and packet dropouts can appear. Hence, the TVDRKF is able to estimate the nonavailable data not only due to the event-triggering sampling but also due to the packet dropouts.…”

Event-triggered predictor-based control with gain-Scheduling and extended state observer for networked control systems

“…For instance, the effect of time-varying delays has been tackled under different control schemes: via state-feedback control [4], multi-rate control [33], state estimators [26], [30], and reset-based control in [2], [31]. Packet dropouts have been faced using predictive control [20], predictor-observer methods [5,8], gain scheduling [9], etc. Packet disordering has also been investigated in [22,23,37] by introducing different packet reordering mechanisms, and in [33,7,6] by using dual-rate control.…”

“…In event-triggered control (ETC) [19,18], data packets are transmitted only when event-based conditions are satisfied. Compared to the traditional time-triggered control, ETC enables to further reduce resource utilization, such as bandwidth and energy consumption [28,8]. Indeed, the design of more efficient ETC strategies is a matter of current research, with interesting applications related to the field of NCS: containment control of multiagent systems [44], leader-following consensus [45] and distributed formation control [12], among others.…”