Kazumune Hashimoto

In this paper, we propose a self-triggered formulation of model predictive control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-and-hold implementation is also given, which guarantees that the state converges t

In this study, the authors propose an aperiodic formulation of model predictive control for distributed agents with additive bounded disturbances. In the proposed method, each agent solves an optimal control problem only when certain control performances cannot be guaranteed according to certain triggering rules. This could lead to the reduction of energy consumption and the alleviation of over usage of communication resources. The triggering rules are derived for both event‐triggered and self‐t

This article investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown transition dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. More

In this paper, we propose a distributed state-and-fault estimation scheme for multiagent systems. The estimator is based on an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Two theoretical results are given to analyze the correctness of our approach. First, we provide a necessary and sufficient condition such that the state and f

In this paper we propose a new event-triggered scheme for nonlinear continuous-time systems with additive bounded disturbances. Unlike existing results, the proposed event-triggered strategy is not derived from Lyapunov stability analysis. Instead, it is obtained from the time interval when the state reaches a local region around the origin. By guaranteeing that this time interval becomes smaller as the optimal control problem is solved, we ensure that the state converges to the prescribed set i

Event-triggered and self-triggered control have been proposed in recent years as promising control strategies to reduce communication resources in networked control systems (NCSs). Based on the notion of set-invariance theory, this note presents new self-triggered control strategies for linear discrete-time systems subject to input and state constraints. The proposed schemes not only achieve communication reduction for NCSs, but also ensure both asymptotic stability of the origin and constraint

In this letter, we investigate novel self-triggered controllers for nonlinear control systems with reachability and safety specifications. To synthesize the self-triggered controller, we leverage the notion of symbolic models, or abstractions, which represent abstracted expressions of control systems. The symbolic models will be constructed through the concepts of approximate alternating simulation relations, based on which, and by employing a reachability game, the self-triggered controller is

We propose control and communication strategies for nonlinear networked control systems subject to state and input constraints. The objective is to steer the state of the system toward a prescribed target set in finite time (reachability), while at the same time remaining inside a safety set for all time (safety). By leveraging the notion of the δ-input-to-state stability (ISS) control Lyapunov function, we derive a sufficient condition to generate a communication scheduling, such that the resul

In this paper, we propose a new self-triggered formulation of Model Predictive Control for continuous-time linear networked control systems. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with different sampling time intervals. The controller then picks up one sampling pattern as a transmission decision, such that a reduction of communication load and the stability will be obtained. T

Symbolic models or abstractions are known to be powerful tools towards the control design of cyber-physical systems (CPSs) with logic specifications. In this paper, we investigate a novel learning-based approach towards the construction of symbolic models for nonlinear control systems. In particular, the symbolic model is constructed based on learning the un-modeled part of the dynamics from training data based on state-space exploration, and the concept of an alternating simulation relation tha

In this paper we propose a new approach to fault-tolerant multiprocessor scheduling by exploiting implicit redundancy, which is originally introduced by task duplication. In the new scheduling algorithm, we adopt two strategies: (1) Some processing elements (PEs) are reserved only for realizing fault-tolerance, and thus are not used for original task scheduling (reserved-scheduling). (2) A set of tasks is partitioned into several disjoint small subsets, and to each subset the algorithm is applie

In recent years, event and self-triggered control have been proposed as energy-aware control strategies to expand the life-time of battery powered devices in Networked Control Systems (NCSs). In contrast to the previous works in which their control objective is to achieve stability, this paper presents a novel energy-aware control scheme for achieving high level specifications, or more specifically, temporal logic specifications. Inspired by the standard hierarchical strategy that has been propo