Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the policy.In the policy improvement process,the policy gradient based method is employed.

Optimal Injection Attack for UCPS Under Vertical Depth-Keeping Task Via Game Approach

Dear Editor, This letter concerns about the security problem of underwater cyber-physical system(UCPS) for depth-keeping task in the vertical plane. A dynamic parametric model of the UCPS with ocean currents and hydrostatic is considered. With the intelligence of the controller, the objective function of a zero-sum game between the attacker and the controller is introduced. The attacker destroys the depth-keeping task of UCPS by injecting the designed false data into the system.

Distributed Control of Nonholonomic Robots Without Global Position Measurements Subject to Unknown Slippage Constraints

This paper studies the fully distributed formation control problem of multi-robot systems without global position measurements subject to unknown longitudinal slippage constraints.It is difficult for robots to obtain accurate and stable global position information in many cases,such as when indoors,tunnels and any other environments where GPS(global positioning system)is denied,thus it is meaningful to overcome the dependence on global position information.Additionally,unknown slippage,which is hard to avoid for wheeled robots due to the existence of ice,sand,or muddy roads,can not only affect the control performance of wheeled robot,but also limits the application scene of wheeled mobile robots.To solve both problems,a fully distributed finite time state observer which does not require any global position information is proposed,such that each follower robot can estimate the leader’s states within finite time.The distributed adaptive controllers are further designed for each follower robot such that the desired formation can be achieved while overcoming the effect of unknown slippage.Finally,the effectiveness of the proposed observer and control laws are verified by simulation results.

Optimal injection attack strategy for cyber-physical systems:a dynamic feedback approach

This paper investigates the system security problem of cyber-physical systems(CPSs),which is not only more practical but also more significant to deal with than the detecting faults problem.The purpose of this paper is to find an optimal attack strat-egy that maximizes the output error of the attacked system with low energy consumption.Based on a general model of linear time-invariant systems and a key technical lemma,a new optimal attack strategy for the meticulously designed false data injection attack is constructed.It is worth mentioning that compared with the existing model-based attack strategies,the designed one is more general and the corresponding attack strategy is more easily implemented when system states and external input are inaccessible.Key to overcom-ing the inaccessible information,a dynamic observer in the form of Luenberger is constructed.Finally,a networked magnetic levitation steel ball movement system is applied to illustrate the effectiveness of the proposed scheme.

Optimal injection attack strategy for cyber-physical systems:a dynamic feedback approach

This paper investigates the system security problem of cyber-physical systems(CPSs),which is not only more practical but also more signi cant to deal with than the detecting faults problem.The purpose of this paper is to nd an optimal attack strategy that maximizes the output error of the attacked system with low energy consumption.Based on a general model of linear time-invariant systems and a key technical lemma,a new optimal attack strategy for the meticulously designed false data injection attack is constructed.It is worth mentioning that compared with the existing model-based attack strategies,the designed one is more general and the corresponding attack strategy is more easily implemented when system states and external input are inaccessible.Key to overcoming the inaccessible information,a dynamic observer in the form of Luenberger is constructed.Finally,a networked magnetic levitation steel ball movement system is applied to illustrate the e ectiveness of the proposed scheme.

Self‐supervised monocular depth estimation via asymmetric convolution block

Without the dependence of depth ground truth,self‐supervised learning is a promising alternative to train monocular depth estimation.It builds its own supervision signal with the help of other tools,such as view synthesis and pose networks.However,more training parameters and time consumption may be involved.This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end‐to‐end manner.The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training.During infer-ence time,the asymmetric kernels are fused and converted to the original network to predict more accurate image depth,thus bringing no extra computations anymore.The network is trained and tested on the KITTI monocular dataset.The evaluated results demonstrate that the depth model outperforms some State of the Arts(SOTA)ap-proaches and can reduce the inference time of depth prediction.Additionally,the pro-posed model performs great adaptability on the Make3D dataset.