This paper investigates the distributed adaptive platoon tracking problem of third-order heterogeneous vehicles subject to model uncertainties. The design process is divided into two steps. Firstly, an adaptive tracki...
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This paper investigates the distributed adaptive platoon tracking problem of third-order heterogeneous vehicles subject to model uncertainties. The design process is divided into two steps. Firstly, an adaptive tracking controller is designed for the dynamic leading vehicle. And then, the distributed adaptive controllers are established for followers. Moreover, the predictor technique is used to improve the estimate performance of the adaptive law, and the total disturbance is approximated and compensated by the variable gain nonlinear extended state observers(NESOs) driven by the estimation error. By introducing the variable gain hyperbolic tangent tracking differentiator(HTTD), the “complexity explosion” problem is avoided. The feasibility and effectiveness of the proposed protocol are verified by simulation tests.
Leighton Chajnantor Telescope(LCT), i.e., the former Caltech Submillimeter Observatory telescope, will be refurbished at the new site in Chajnantor Plateau, Chile in 2023. The environment of LCT will change significan...
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Leighton Chajnantor Telescope(LCT), i.e., the former Caltech Submillimeter Observatory telescope, will be refurbished at the new site in Chajnantor Plateau, Chile in 2023. The environment of LCT will change significantly after its relocation, and the telescope will be exposed to large wind disturbances directly because its enclosure will be completely open during observation. The wind disturbance is expected to be a challenge for LCT's pointing control since the existing control method cannot reject this disturbance very well. Therefore, it is very necessary to develop a new pointing control method with good capability of disturbance rejection. In this research, a disturbance observer—based composite position controller(DOB-CPC) is designed, in which an H∞feedback controller is employed to compress the disturbance, and a feedforward linear quadratic regulator is employed to compensate the disturbance precisely based on the estimated disturbance signal. Moreover, a controller switching policy is adopted, which applies the proportional controller to the transient process to achieve a quick response and applies the DOB-CPC to the steady state to achieve a small position error. Numerical experiments are conducted to verify the good performance of the proposed pointing controller(i.e., DOB-CPC) for rejecting the disturbance acting on LCT.
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti...
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This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
In this paper,a novel adaptive Fault-Tolerant control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter *** strategy is based on the output rede...
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In this paper,a novel adaptive Fault-Tolerant control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter *** strategy is based on the output redefinition method and Adaptive Dynamic Programming(ADP).The intelligent FTC scheme consists of two main parts:a basic fault-tolerant and stable controller and an ADP-based supplementary *** the basic FTC part,an output redefinition approach is designed to make zero-dynamics stable with respect to the new ***,Ideal Internal Dynamic(IID)is obtained using an optimal bounded inversion approach,and a tracking controller is designed for the new output to realize output tracking of the nonminimum phase HSV *** the ADP-based compensation control part,an ActionDependent Heuristic Dynamic Programming(ADHDP)adopting an actor-critic learning structure is utilized to further optimize the tracking performance of the HSV control ***,simulation results are provided to verify the effectiveness and efficiency of the proposed FTC algorithm.
Strongly stealthy cyber-attacks in networked vehicle queue (V2V) network environments often manifest themselves as small changes to data, which makes it difficult for existing methods to effectively detect such attack...
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In this paper,the authors consider the inverse problem for the Moore-Gibson-Thompson equation with a memory term and variable diffusivity,which introduce a sort of delay in the dynamics,producing nonlocal effects in *...
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In this paper,the authors consider the inverse problem for the Moore-Gibson-Thompson equation with a memory term and variable diffusivity,which introduce a sort of delay in the dynamics,producing nonlocal effects in *** H¨older stability of simultaneously determining the spatially varying viscosity coefficient and the source term is obtained by means of the key pointwise Carleman estimate for the Moore-Gibson-Thompson *** the sake of generality in mathematical tools,the analysis of this paper is discussed within the framework of Riemannian geometry.
This study presents a novel application of computer vision technology to enhance safety in the automotive service industry, with a specific focus on the real-time detection of Personal Protective Equipment (PPE) usage...
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The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a stat...
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The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the *** method uses the physical,integration,and optimization layers as a three-layer modeling *** physical layer is used to describe the physical models of energy units in the *** the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are *** optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target *** simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.
This article presents developments by its authors: the IndustrialKit library package and the RCWorkspace application based on it. The library allows developers of third-party applications to use the functionality of p...
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In this study, a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy, which solves the cooperative optimal output regulation problem for linear discretetime mult...
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In this study, a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy, which solves the cooperative optimal output regulation problem for linear discretetime multi-agent systems. Notably, the dynamics of all the agent systems and exo-system is completely unknown. By combining adaptive dynamic programming with an internal model, a model-free off-policy learning method is proposed to estimate the optimal control gain and the distributed adaptive internal model by only accessing the measurable data of multi-agent systems. Moreover, different from the traditional cooperative adaptive controller design method, a distributed internal model is approximated online. Convergence and stability analyses show that the estimate controller generated by the proposed data-driven learning algorithm converges to the optimal distributed controller. Finally, simulation results verify the effectiveness of the proposed method.
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