This paper researches the synchronisation problem of complex dynamic networks with time-varying coupling delays based on sampled datacontrol. First, a new Lyapunov-Krasovskii function (LKF) is constructed and then li...
详细信息
ISBN:
(纸本)9798350321050
This paper researches the synchronisation problem of complex dynamic networks with time-varying coupling delays based on sampled datacontrol. First, a new Lyapunov-Krasovskii function (LKF) is constructed and then linear matrix inequality (LMI) is gained using Wirtinger's inequality. Then, by solving the LMI, the unconservative condition for guaranteeing the synchronisation of a time-varying coupled time delay complex network for the control of sampled data is obtained. Finally, the example of numerical simulations shows that has broad application prospects.
This paper studies the optimal tracking control problem of discrete-time linear multi-input systems from the perspective of Non-Zero-Sum Games (NZSG) using reinforcement Q-learning technique. Firstly, an augmented mul...
详细信息
ISBN:
(纸本)9798350321050
This paper studies the optimal tracking control problem of discrete-time linear multi-input systems from the perspective of Non-Zero-Sum Games (NZSG) using reinforcement Q-learning technique. Firstly, an augmented multi-input systems is constructed by combining the original multi-input systems and the reference trajectory dynamics. Then, the original optimal tracking control problem can be transformed into the NZSG optimal control problem of the constructed augmented multi-input systems. In order to obtain the Nash equilibrium solution of the NZSG optimal control problem, a Q-function is introduced and an reinforcement Q-learning algorithm is designed to learn the Nash equilibrium solution. The convergence of the reinforcement Q-learning algorithm is also given. Finally, a simulation example is given to verify the effectiveness of the proposed reinforcement Q-learning algorithm.
The exploration-exploitation tradeoff is an inherent challenge in data-driven adaptive control. Though this tradeoff has been studied for multiarmed bandits (MABs) and reinforcement learning for linear systems, it is ...
详细信息
The exploration-exploitation tradeoff is an inherent challenge in data-driven adaptive control. Though this tradeoff has been studied for multiarmed bandits (MABs) and reinforcement learning for linear systems, it is less well studied for learning-based control of nonlinear systems. A significant theoretical challenge in the nonlinear setting is that there is no explicit characterization of an optimal controller for a given set of cost and system parameters. We propose the use of a finite-horizon oracle controller with full knowledge of parameters as a reasonable surrogate to an optimal controller. This allows us to develop policies in the context of learning-based model-predictive control (MPC) and conduct a control-theoretic analysis using techniques from MPC and optimization theory to show that these policies achieve low regret with respect to this finite-horizon oracle. Our simulations exhibit the low regret of our policy on a heating, ventilation, and air-conditioning model with partially unknown cost function.
This paper proposes a model-free adaptive control (MFAC) strategy for the internal mixer temperature (IMT) systems with characteristics of large time lags, large inertia, time varying and disturbances. A dynamic linea...
详细信息
ISBN:
(纸本)9798350321050
This paper proposes a model-free adaptive control (MFAC) strategy for the internal mixer temperature (IMT) systems with characteristics of large time lags, large inertia, time varying and disturbances. A dynamic linearization method is applied to reconstruct the IMT system into a linear incremental form to facilitate the consequent controller design. Both input saturation and event-triggering condition are considered in the proposed algorithm, where the former is added to avoid overshooting and the latter is used to reduce the execution number of controller updates. In addition, the unknown parameter in the obtained linear model can be identified by the proposed estimation algorithm using only I/O data. The effectiveness of the proposed MFAC is verified through simulations.
This work solves the localization estimation of dynamic multi-agent systems (MASs) with sensor multiplicative failures, which is more general yet challenging to address than static sensor networks with ideal condition...
详细信息
This work solves the localization estimation of dynamic multi-agent systems (MASs) with sensor multiplicative failures, which is more general yet challenging to address than static sensor networks with ideal conditions. Barycentric coordinate is introduced to characterize the relative positions between agents. A new linear data model is constructed to represent the relationship between barycentric coordinates and relative distance. Based on the linear model, an adaptive parameter estimation algorithm is designed, and then it is applied to solve the relative distance compensation problem of the MASs with sensor multiplicative failures. Using the estimated parameter, a data-driven adaptive distributed localization estimation scheme based on iterative learning is proposed, in which only the measured relative distance data are available instead of the system model information. A key to obtaining accurate localization is overcoming the difficulties from inaccurate relative distance variables due to sensor failure via the data-driven adaptive relative distance compensation method. The numerical examples and experimental results verify the effectiveness of the proposed methods.
In this paper, a deep reinforcement learning-based speed and heading control method is proposed for an unmanned surface vehicle (USV). A deep deterministic policy gradient (DDPG) algorithm which combines with an actor...
详细信息
ISBN:
(纸本)9798350321050
In this paper, a deep reinforcement learning-based speed and heading control method is proposed for an unmanned surface vehicle (USV). A deep deterministic policy gradient (DDPG) algorithm which combines with an actor-critic reinforcement learning mechanism, is adopted to provide continuous control variables by interacting with the environment. Moreover, two types of reward functions are created for speed and heading control of the USV. The control policy is trained by trial and error so that the USV can be guided to achieve the desired speed and heading angle steadily and rapidly. Simulation results verify the feasibility and effectiveness of the proposed approach by comparisons with classical PID control and S plane control.
This brief studies the optimal control policy learning problem for discrete-time linear systems. A data-driven model-free algorithm is proposed by using the data matrices of the augmented system state and the increasi...
详细信息
This brief studies the optimal control policy learning problem for discrete-time linear systems. A data-driven model-free algorithm is proposed by using the data matrices of the augmented system state and the increasing of the discount factor. The control gains generated by the proposed algorithm are proven to converge to the optimal one. Compared with the existing work, our model-free algorithm avoids the dependence on initial stabilizing control policy and the use of Kronecker product. Some numerical examples are provided to illustrate the proposed algorithm and analysis results.
This paper discusses the model-free adaptive control for nonlinear systems under sparse sensor attacks. Firstly, it is proposed that there are multiple transmission channels in the sensor-to-controller transmission ne...
详细信息
ISBN:
(纸本)9798350321050
This paper discusses the model-free adaptive control for nonlinear systems under sparse sensor attacks. Firstly, it is proposed that there are multiple transmission channels in the sensor-to-controller transmission network. Secondly, system sensors are affected by DoS attacks and FDI attacks. Then, a channel switching mechanism is used to adjust the channel to compensate for adverse effects of attacks. Finally, it is proved that the tracking error of the system converges to a tiny constant and a numerical simulation example demonstrates the validity of the proposed method.
Automated Guided vehicles (AGVs) provide a better solution to hospital logistics. In this paper, a mathematical model for point-to-point pickup and delivery tasks in a hospital with time windows and capacity constrain...
详细信息
ISBN:
(纸本)9798350321050
Automated Guided vehicles (AGVs) provide a better solution to hospital logistics. In this paper, a mathematical model for point-to-point pickup and delivery tasks in a hospital with time windows and capacity constraints based on heterogeneous AGVs fleet is established, and a meta-heuristic algorithm based on ALNS is designed to solve the static scheduling problem of AGVs in the hospital environment. The effectiveness of the proposed algorithm is verified by numerical experiments and comparison with the basic algorithm. Finally, we summarized the direction of the further work.
In this work, the stability analysis and stabilization problem for one class of discrete-time cyber-physical systems (CPSs) under denial-of-service (DoS) attacks is investigated. Firstly, according to appropriate form...
详细信息
ISBN:
(纸本)9798350321050
In this work, the stability analysis and stabilization problem for one class of discrete-time cyber-physical systems (CPSs) under denial-of-service (DoS) attacks is investigated. Firstly, according to appropriate formulation of DoS attacks, different scenarios based on the presence or absence of DoS attacks are developed. Then the input-to-state stability (ISS) and globally asymptotical stability (GAS) of the considered CPSs can be guaranteed in terms of DoS frequency and duration restrictions, respectively. Finally, one example is given to verify the applicability of our theoretical result.
暂无评论