The impact of the Rural Minimum Living Standard Guarantee(Rural Dibao)Program on child nutrition outcomes

The Rural Minimum Living Standard Guarantee(Rural Dibao)is an important unconditional cash transfer program to alleviate poverty in rural China.Despite the importance of children’s nutrition in breaking poverty cycles,little is known about the impact of Rural Dibao on child nutrition outcomes.Using China Family Panel Studies(CFPS),this paper examines the effects of Rural Dibao on child nutrition outcomes and investigates potential pathways and heterogeneous effects.We exploit propensity score matching and difference-in-differences techniques to evaluate the effects of the Rural Dibao program on child nutrition outcomes.Our results suggest that Rural Dibao significantly impacts the nutrition outcomes of children up to 15 years of age.Specifically,our results suggest that Rural Dibao improves child height-to-age z-scores by 1.05 standard deviations and lowers the probability of stunting by 11.9 percentage points.Additional analyses suggest that increased protein intake is the main pathway through which Rural Dibao participation contributes to better nutrition outcomes.We also find that the effect of the program is more pronounced among girls,children who are non-left-behind or live with highly educated mothers,and those from low-income families and poor areas.Our findings suggest that Rural Dibao participation helps improve child nutrition outcomes through improving diet quality.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

Guarantee Strategy for the Safety of Agricultural Industry in Hunan Province

The safety of agricultural industry in Hunan Province shows an upward trend from"basically safe"to"very safe",but the state in the"safe"or"very safe"range is still unstable.In view of this,Hunan Province should guarantee the agricultural production ability,cultivate and enhance the core competitiveness of agriculture,firmly grasp the agricultural control power,attach importance to the export quality of agri-cultural products and other aspects to ensure the safety of agricultural industry.

Research on Guaranteed Scheduling Algorithms

Guaranteed scheduling is necessary for hard real time systems, because each process of them must meet their deadline, or a serious consequence will result.In this paper we discuss two guaranteed schedule algorithms:backtracking and branch and bound,introduce the process to find the optimal solution by both methods,propose the concept of logical node and physical node.Through some experiments for different process sets,we have made comparisons between the two algorithms in branch nodes,comparing time,topology of the search tree,memory size needed,etc.

Decentralized robust guaranteed cost control for a class of interconnected singular large-scale systems with time-delay and parameter uncertainty

The decentralized robust guaranteed cost control problem is studied for a class of interconnected singular large-scale systems with time-delay and norm-bounded time-invariant parameter uncertainty under a given quadratic cost performance function. The problem that is addressed in this study is to design a decentralized robust guaranteed cost state feedback controller such that the closed-loop system is not only regular, impulse-free and stable, but also guarantees an adequate level of performance for all admissible uncertainties. A sufficient condition for the existence of the decentralized robust guaranteed cost state feedback controllers is proposed in terms of a linear matrix inequality （LMI） via LMI approach. When this condition is feasible, the desired state feedback decentralized robust guaranteed cost controller gain matrices can be obtained. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed approach.

Guaranteed Cost Consensus for High-dimensional Multi-agent Systems With Time-varying Delays

Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for high-dimensiona multi-agent systems with time-varying delays, where a cos function is defined based on state errors among neighboring agents and control inputs of all the agents. By the state space decomposition approach and the linear matrix inequality(LMI)sufficient conditions for guaranteed cost consensus and consensu alization are given. Moreover, a guaranteed cost upper bound o the cost function is determined. It should be mentioned that these LMI criteria are dependent on the change rate of time delays and the maximum time delay, the guaranteed cost upper bound is only dependent on the maximum time delay but independen of the Laplacian matrix. Finally, numerical simulations are given to demonstrate theoretical results.

Delay-independent robust guaranteed-cost control for uncertain linear neutral systems

This article concerns the delay-independent guaranteed-cost control problem via memoryless state feedback for a class of neutral-type systems with structural uncertainty and a given quadratic cost function. New delay-independent conditions for the existence of the guaranteed-cost controller are presented in the term of LMIs. An algorithm involving optimization is proposed to design a controller achieving an optimal guaranteed-cost, such that, the system can be stabilized for all admissible uncertainties. A numerical example is provided to illustrate the feasibility of the proposed method.

Guaranteed cost control with constructing switching law of uncertain discrete-time switched systems

A guaranteed cost control problem for a class of linear discrete-time switched systems with norm-bounded uncertainties is considered in this article. The purpose is to construct a switching rule and design a state feedback control law, such that, the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound for all admissible uncertainties under the constructed switching rule. A sufficient condition for the existence of guaranteed cost controllers and switching rules is derived based on the Lyapunov theory together with the linear matrix inequality （LMI） approach. Furthermore, a convex optimization problem with LMI constraints is formulated to select the suboptimal guaranteed cost controller. A numerical example demonstrates the validity of the proposed design approach.

Robust reliable guaranteed cost control for uncertain singular systems with time-delay

The robust reliable guaranteed cost control for uncertain singular delay systems with actuator failures and a given quadratic cost function is studied. The system under consideration involves constant time-delay and norm-bounded parameter uncertainties. The purpose is to design state feedback controllers which can tolerate actuator failure, such that the closed-loop system is stable, and the specified cost function has an upper bound for all admissible uncertainties. The sufficient conditions for the solvability of this problem are obtained by a linear matrix inequality （LMI） method. Furthermore, a numerical example is given to demonstrate the applicability of the proposed approach.

Non-fragile guaranteed cost control of discrete-time fuzzy bilinear system

This paper focuses on the problem of non-fragile guaranteed cost control for a class of T-S discrete-time fuzzy bilinear systems（DFBS）.Based on the parallel distributed compensation（PDC） approach,the sufficient conditions are derived such that the closed-loop system is asymptotically stable and the cost function value is no more than a certain upper bound in the presence of the additive controller gain perturbations.The non-fragile guaranteed cost controller can be obtained by solving a set of bilinear matrix inequalities（BMIs）.The Van de Vusse model is utilized to demonstrate the validity and effectiveness of the proposed approach.

Beaconless Position-based Routing with Guaranteed Delivery for Wireless Ad hoc and Sensor Networks

Existing position-based routing algorithms, where packets are forwarded in the geographic direction of the destination, normally require that the forwarding node should know the positions of all neighbors in its transmission range. This information on direct neighbors is gained by observing beacon messages that each node sends out periodically. Several beaconless greedy routing schemes have been proposed recently. However, none of the existing beaconless schemes guarantee the delivery of packets. Moreover, they incur communication overhead by sending excessive control messages or by broadcasting data packets. In this paper, we describe how existing localized position based routing schemes that guarantee delivery can be made beaconless, while preserving the same routes. In our guaranteed delivery beaconless routing scheme, the next hop is selected through the use of control RTS/CTS messages and biased timeouts. In greedy mode, the neighbor closest to destination responds first. In recovery mode, nodes closer to the source will select shorter timeouts, so that other neighbors, overhearing CTS packets, can eliminate their own CTS packets if they realize that their link to the source is not part of Gabriel graph. Nodes also cancel their packets after receiving data message sent by source to the selected neighbor. We analyze the behavior of our scheme on our simulation environment assuming ideal MAC, following GOAFR+ and GFG routing schemes. Our results demonstrate low communication overhead in addition to guaranteed delivery.

Guaranteed Cost Active Fault-tolerant Control of Networked Control System with Packet Dropout and Transmission Delay

The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet dropout and transmission delay, a piecewise constant controller is adopted. With a guaranteed cost function, optimal controllers whose number is equal to the number of actuators are designed, and the design process is formulated as a convex optimal problem that can be solved by existing software. The control strategy is proposed as follows： when actuator failures appear, the fault detection and isolation unit sends out the information to the controller choosing strategy, and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen. Two illustrative examples are given to demonstrate the effectiveness of the proposed approach. By comparing with the existing methods, it can be seen that our method has a better performance.

Nonfragile guaranteed cost control for Delta operator-formulated uncertain time-delay systems

With consideration that the controller parameters may vary from the designed value when the controller is realized, based on Lyapunov stability theory, a design method of nonfragile guaranteed cost control for a class of Delta operator-formulated uncertain time-delay systems is studied. A sufficient condition for the existence of the nonfragile guaranteed cost controller is given. A numeric example is then given to illustrate the effectiveness and the feasibility of the designed method. The results show that even if the parameters of the designed controller are of variations, the closed-loop system is still asymptotically stable and the super value of the cost function can also be obtained, while the closed-loop system will be unstable if the variations of the controller parameters are not considered when the controller is designed. The nonfragile guaranteed cost controller derived from the traditional shift operator method may cause the closed-loop system to be unstable, while the nonfragile guaranteed cost controller based on Delta operator method can avoid the unstable problem of the closed-loop system.

Delay-dependent guaranteed cost control for uncertain systems with both state and input delays

This paper considers the guaranteed cost control problem for a class of uncertain linear systems with both state and input delays. By representing the time-delay system in the descriptor system form and using a recent result on bounding of cross products of vectors, we obtain new delay-dependent sufficient conditions for the existence of the guaranteed cost controller in terms of linear matrix inequalities. Two examples are presented which show the effectiveness of our approach.

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

Optimal guaranteed cost control for fuzzy descriptor systems with time-varying delay

Based on the delay-independent rule, the problem of optimal guaranteed cost control for a class of Takagi-Sugeno （T-S） fuzzy descriptor systems with time-varying delay is studied. A linear quadratic cost function is considered as the performance index of the closed-loop system. Sufficient conditions for the existence of guaranteed cost controllers via state feedback are given in terms of linear matrix inequalities （LMIs）, and the design of an optimal guaranteed cost controller can be reduced to a convex optimization problem. It is shown that the designed controller not only guarantees the asymptotic stability of the closed-loop fuzzy descriptor delay system, but also provides an optimized upper bound of the guaranteed cost. At last, a numerical example is given to illustrate the effectiveness of the proposed method and the perfect performance of the optimal guaranteed cost controller.

Event-triggered robust guaranteed cost control for two-dimensional nonlinear discrete-time systems

An event-triggered scheme is proposed to solve the problems of robust guaranteed cost control for a class of two-dimensional(2-D)discrete-time systems.Firstly,an eventtriggered scheme is proposed for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities.Then,according to the Lyapunov functional method,the sufficient conditions for the existence of event-triggered robust guaranteed cost controller for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities are given.Furthermore,based on the sufficient conditions and the linear matrix inequality(LMI)technique,the problem of designing event-triggered robust guaranteed cost controller is transformed into a feasible solution problem of LMI.Finally,a numerical example is given to demonstrate that,under the proposed event-triggered robust guaranteed cost control,the closed-loop system is asymptotically stable and fewer communication resources are occupied.

Non-fragile hybrid guaranteed cost control for a class of uncertain switched linear systems

This paper focuses on the problem of non-fragile hybrid guaranteed cost control for a class of uncertain switched linear systems. The controller gain to be designed is assumed to have additive gain variations. Based on multiple-Lyapunov function technique, the design of non-fragile hybrid guaranteed cost controllers is derived to make the corresponding closed-loop system asymptotically stable for all admissible uncertainties. Furthermore, a convex optimization approach with LMIs constraints is introduced to select the optimal non-fragile guaranteed cost controllers. Finally, a simulation example illustrates the effectiveness of the proposed approach.

Guaranteed cost control for T-S fuzzy systems with time-varying delays

This paper deals with the problem of guaranteed cost control for nonlinear systems with time-varying delays which is represented by Takagi-Sugeno （T-S） fuzzy models with time-varying delays.The derivatives of time-varying delay are not necessary to be bounded.Based on the free weighting matrix method,sufficient conditions for the existence of fuzzy guaranteed cost controller via state feedback are given in terms of linear matrix inequalities （LMIs）.A minimizing method is also proposed to search the suboptimal upper bound of the guaranteed cost function.The results are delay-dependent but contain delay-independent criteria as a special case.A numerical example is presented to demonstrate the effectiveness and less conservativeness of our work.