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In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles' tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis of discrete‐time systems with a time‐varying delay. The conservatism and computation burden are two important factors to evaluate a stability condition. By taking the relationship of two reciprocally convex parts into consideration, a new combined matrix‐separation‐based inequality is proposed that involves only a few free matrices. Moreover, an improved matrix‐injection‐based transformation lemma with the parameter varying within a closed interval is proposed by introducing only one free matrix. By constructing an appropriate Lyapunov–Krasovskii functional and applying the improved methods, a relaxed stability condition is consequently obtained with a small number of decision variables. Two numerical examples are given to show the merits of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes a Modified model reference adaptive controller and observer (MMRACO) for unknown linear time‐invariant (LTI) systems. The recent literature, and in particular the so‐called MRACO approach, has demonstrated how some deficiencies in standard model reference adaptive control can be overcome by using a combination of modified reference models and observers, but this is done at the expense of knowing bounds on certain unknown parameters. Here, a novel adaptive observer/controller combination is presented which removes the need to know the above bounds but preserves the other desirable features of the MRACO approach. A Lyapunov analysis of the closed‐loop system ensures asymptotic convergence of both tracking and observer errors, with uniform boundedness of parameter estimation errors. A key advantage of the scheme is that the design involves only solutions of two Lyapunov equations (or linear matrix inequalities) and does not require the selection of a "high gain" parameter. The effectiveness of the approach is illustrated in some numerical examples. [ABSTRACT FROM AUTHOR]

Abstract In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

Abstract This paper is concerned with the stability analysis of discrete‐time systems with a time‐varying delay. The conservatism and computation burden are two important factors to evaluate a stability condition. By taking the relationship of two reciprocally convex parts into consideration, a new combined matrix‐separation‐based inequality is proposed that involves only a few free matrices. Moreover, an improved matrix‐injection‐based transformation lemma with the parameter varying within a closed interval is proposed by introducing only one free matrix. By constructing an appropriate Lyapunov–Krasovskii functional and applying the improved methods, a relaxed stability condition is consequently obtained with a small number of decision variables. Two numerical examples are given to show the merits of the proposed methods.

This study presents a systematic methodology for developing a stabilizing controller for a general hybrid systems model. The approach is based on utilizing the small‐gain theorem as a means of constructing the Lyapunov function and analyzing the input–output stability of the subsystems in the feedback loop. By considering the control system in a closed‐loop configuration with the hybrid system, the small‐gain theorem can be applied. In this scheme, a dynamic control system is proposed that satisfies the closed‐loop stability conditions. This method applies to various hybrid systems' applications due to its generality. To demonstrate the effectiveness and performance of the proposed control approach, two simulation examples, including a linear hybrid system and a bipedal walking robot, are examined. [ABSTRACT FROM AUTHOR]

This paper addresses the topic of stabilizing a class of nonholonomic systems in chained form impacted by matched uncertainties and time-varying perturbations. To design efficient stabilizing control law, the whole problem is divided into two sub-problems. The first sub-problem considers a system with perturbation stabilized using a fixed-time controller. The second sub-problem consists of the design of a controller for a second system operating under perturbations and uncertainties. To control this second system, firstly a terminal sliding mode manifold is proposed to achieve a fixed-time stability. As opposed to the traditional supertwisting algorithm (STA), the present work proposes a generalized STA (GSTA). The proposed method’s most striking feature is the substitution of a fractional power term for the standard STA’s discontinuous term, which can significantly boost the former’s performance. It is demonstrated that the GSTA will reduce to the traditional STA if the fractional power in the nonsmooth term equals −1/2. The ability of the sliding variables to finite-time converge to an arbitrary tiny region in the vicinity of the origin by adjusting the gains and the fractional power will be thoroughly proven under the GSTA by utilizing strict Lyapunov analysis. The effectiveness of the proposed control method is shown through extensive simulations.

In this study, a robust adaptive event‐triggered iterative learning control (ET‐ILC) method is developed for discrete‐time non‐repetitive nonlinear systems with both time‐varying system parameters and time‐iteration‐varying non‐parametric uncertainty. The proposed method utilizes an iteration‐varying dead‐zone function to estimate both parametric and non‐parametric uncertainties simultaneously. Moreover, an event‐triggered control mechanism including a mixed triggering strategy is employed to minimize the unnecessary usage of communication resource. This strategy updates the controller only when the triggering condition is met, thereby reducing the frequency of controller actions. The results are also extended to a class of multiple‐input–multiple‐output (MIMO) non‐repetitive nonlinear systems. The designed robust adaptive ET‐ILC algorithm not only guarantees perfect learning convergence in the iteration domain but also contributes to saving the system resources. The effectiveness of the presented method is demonstrated by virtue of the rigorous theoretical analysis and the illustrative simulation examples. [ABSTRACT FROM AUTHOR]