王涛

个人信息

参与实验室科研项目

基于数据驱动和联合设计的无线网络化控制系统的使能建模和设计

研究课题

基于近似模型的模型预测控制、基于事件触发的模型预测控制/分布式模型预测控制

奖励荣誉

2020硕士研究生国家奖学金

学术成果

共撰写/参与撰写专利 0 项，录用/发表论文 11 篇，投出待录用论文1篇。学术成果部分从赵云波教授个人维护的bib文件自动生成，只包含其共同署名的论文/专利（联合培养或代为指导学生可能有未署名论文/专利，不会在此展示），会因为更新不及时而缺失部分论文/专利，如有缺失请及时与老师联系添加更新。

Journal Articles

1. Compound Event-Triggered Distributed MPC for Coupled Nonlinear Systems Yu Kang, Tao Wang, Pengfei Li, Zhenyi Xu, and Yun-Bo Zhao IEEE Trans. Cybern. 2023 [Abs] [doi] [pdf]

   This paper investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. As a result, the number of triggering instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

2. Disturbance Prediction-Based Adaptive Event-Triggered Model Predictive Control for Perturbed Nonlinear Systems Pengfei Li, Yu Kang, Tao Wang, and Yun-Bo Zhao IEEE Trans. Automat. Contr. 2023 [Abs] [doi] [pdf]

   A disturbance prediction based adaptive event-triggered model predictive control scheme is proposed for nonlinear systems in the presence of slowly varying disturbance. The optimal control problem in the model predictive control scheme is formulated by taking advantage of a proposed central path-based disturbance prediction approach, and the event-triggered mechanism is designed to be adaptive to the triggering interval. As a result, the proposed scheme improves the state prediction precision and hence reduces greatly the triggering frequency. Furthermore, for input-affine nonlinear systems, the disturbance separation and compensation techniques are developed to further enlarge the triggering interval. Theoretical analysis of the algorithm feasibility and closed-loop stability, as well as numerical evaluations of the effectiveness of the proposed schemes, are also given.

3. Event-Based Model Predictive Control for Nonlinear Systems with Dynamic Disturbance Pengfei Li, Tao Wang, Yu Kang, Kun Li, and Yun-Bo Zhao Automatica 2022 [Abs] [doi] [pdf]

   In this paper, we investigate the event-based model predictive control (MPC) for constrained nonlinear systems with dynamic disturbance. An event-triggered disturbance prediction MPC (DPMPC) scheme and a self-triggered counterpart, which explicitly consider the disturbance dynamics, are proposed. For the event-triggered DPMPC scheme, the triggering condition relying on the state prediction error and the predicted disturbance sequence, updates at each time step based on the system states. For the self-triggered DPMPC scheme, the next triggering instant is determined by using the optimal state sequence and predicted disturbance sequence. In both event-based schemes, the optimal control problems are solved only at triggering instants, thus reducing the consumption of computational resources. The effectiveness of the two schemes is demonstrated by a simulation example.

4. A Novel Self-Triggered MPC Scheme for Constrained Input-Affine Nonlinear Systems Pengfei Li, Yu Kang, Yun-Bo Zhao , and Tao Wang IEEE Trans. Circuits Syst. II 2021 [Abs] [doi] [pdf]

   This brief develops a novel self-triggered model predictive control algorithm based on time delay estimation for perturbed input-affine nonlinear systems. At each triggering instant, the algorithm determines simultaneously the predictive control sequence to feedforward compensate for the disturbance and the next triggering instant. As a consequence, the unnecessary samplings and transmissions are suppressed, and the frequency of solving the model predictive controller is reduced. The feasibility of the scheme as well as the associated stability are verified, with a numerical example illustrating the effectiveness of the proposed scheme.

5. Networked Dual-Mode Adaptive Horizon MPC for Constrained Nonlinear Systems Pengfei Li, Yu Kang, Yun-Bo Zhao , and Tao Wang IEEE Trans. Syst. Man Cybern, Syst. 2021 [Abs] [doi] [pdf]

   This article investigates the predictive control scheme and related stability issue for a class of discrete-time perturbed nonlinear system with state and input constraints. First, we propose a novel control framework, i.e., networked dual-mode adaptive horizon model predictive control (MPC), which consists of a local controller, a remote controller that is subject to packet losses, and a judger coordinating the switchings between them. The optimization procedure of MPC with variable prediction horizon is implemented in the remote controller while a simple state-feedback control law is in the local one. Second, to establish the stability condition, we propose a new Lyapunov function. By specifying the relation between the Lyapunov function and the optimal MPC value function, the input-to-state practical stability is established. Finally, simulation results show the effectiveness of our proposed control scheme.

6. Robust Approximation-Based Event-Triggered MPC for Constrained Sampled-Data Systems Tao Wang, Yu Kang, Pengfei Li, Yun-Bo Zhao , and Peilong Yu J Syst Sci Complex 2021 [Abs] [doi] [pdf]

   In this paper, an approximation-based event-triggered model predictive control (AETMPC) strategy is proposed to implement event-triggered model predictive control for continuous-time constrained nonlinear systems under the digital platform. In our AETMPC strategy, both of the optimal control problem (OCP) and the triggering conditions are defined in discrete-time manner based on approximate discrete-time models, while the plant under control is continuous time. In doing so, sensing load is alleviated because the triggering condition does not need to be checked continuously, and the computation of the OCP is simpler since which is calculated in the discrete-time framework. Meanwhile, robust constraints are satisfied in continuous-time sense by taking inter-sampling behaviour into consideration, and a novel constraint tightening approach is presented accordingly. Furthermore, the feasibility the AETMPC strategy is analyzed and the associated stability of the overall system is established. Finally, this strategy is validated by a numerical example.

7. Robust Model Predictive Control for Constrained Networked Nonlinear Systems: An Approximation-Based Approach Tao Wang, Yu Kang, Pengfei Li, Yun-Bo Zhao , and Peilong Yu Neurocomputing 2020 [doi] [pdf]

Conference Articles

1. Equipment Health Assessment Based on AHP-CRITIC Dynamic Weight Yunsheng Zhao, Pengfei Li, Tao Wang, Yu Kang, and Yun-Bo Zhao In 2022 41st Chin. Control Conf. CCC 2022 [Abs] [doi] [pdf]

   Prognostics Health and Management (PHM) has become a hot research problem with the improvement of different equipment. Besides, it is significant to assess the health status of equipment in PHM because an accurate health assessment can guide maintenance plans for engineers. To accurately reflect equipment health status by an index, an assessment method based on AHP-CRITIC dynamic weight is proposed in this paper. Analytic Hierarchy Process (AHP) is a subjective method used to evaluate the importance of different indicators. The criteria importance through inter-criteria correlation (CRITIC) method is used to calculate the contrast intensity of the same indicator and the conflict between indicators and obtain the objective weights. A set of more scientific weights is gained by combining the weights obtained from AHP and CRITIC, respectively. Moreover, to reflect each indicator’s real impact on overall health status, a dynamic weight adjustment mechanism is used. The case study of suction nozzles of a specific type of chip mounter shows that this method can reflect the health status accurately.

2. Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems: An Iterative Implementation Tao Wang, Pengfei Li, Yu Kang, and Yun-Bo Zhao In 2021 60th IEEE Conf. Decis. Control CDC 2021 [Abs] [doi] [pdf]

   In this paper, a novel iterative self-triggered model predictive control strategy is proposed for continuous-time nonlinear systems with external disturbance. For this strategy, the triggering instants are determined by iteratively using the self-triggered mechanism. To be specific, the triggering mechanism, on the one hand, determines the next sampling instants of the sensor by a prespecified condition, and, on the other hand, decides whether or not to treat the current sampling instant as the triggering instant. Without continuous monitoring of the state, the sensing cost of the sensor can be alleviated. The utilization of the sampling states after the triggering instant leads to a larger triggering interval, and the computational load of the controller can thus be reduced. The effectiveness of the proposed strategy is validated by a numerical example.

3. Approximation-Based Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems Chang Xu, Yu Kang, Yun-Bo Zhao , Pengfei Li, and Tao Wang In 2021 China Autom. Congr. CAC 2021 [Abs] [doi] [pdf]

   This paper proposes an approximation-based selftriggered model predictive control strategy for perturbed constrained nonlinear sampled-data systems. In our proposed strategy, the finite horizon optimal control problem (FHOCP) and the triggering condition are designed based on approximate discrete-time models. By implementing the strategy, the computation problem of the FHOCP becomes tractable since it is computed in a discrete-time framework. Meanwhile, the next triggering instant is pre-determined by the triggering condition, reducing the sensing cost and the computing frequency of the FHOCP. Furthermore, feasibility of the FHOCP and stability of the overall system are analyzed. Finally, a simulation example verifies the effectiveness of the strategy.

4. Event-Triggered Adaptive Horizon Model Predictive Control for Perturbed Nonlinear Systems Pengfei Li, Tao Wang, Yu Kang, and Yun-Bo Zhao In 2020 59th IEEE Conf. Decis. Control CDC 2020 [Abs] [doi] [pdf]

   This paper proposes a new event-triggered adaptive horizon model predictive control (MPC) for discrete-time nonlinear systems with additive disturbance. With the eventtriggered control scheme, the MPC is solved only at triggering instant and the event is triggered if the difference between the actual state and the predicted state exceeds the triggering threshold. The triggering threshold depends on the prediction horizon and becomes larger as the state approaches the terminal constraint set. Therefore, larger triggering intervals can then be obtained. Finally, a numerical example shows the effectiveness of the proposed scheme.