Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of O...Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation;adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.展开更多
When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system＇s dynamic behaviors because it involves multi-disciplinary subsystems. Cur...When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system＇s dynamic behaviors because it involves multi-disciplinary subsystems. Currently, a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge： spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1） a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2） a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1） can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2） effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.展开更多
On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the esse...On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the essential governing equations are presented. This work provides a new method to achieve the numerical solutions of the mechanic of finite deformation.展开更多
This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Tw...This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Two different methods for discretizing the control expression are compared: Euler transformation and bilinear transformation. The sampling rate’s impact on the algorithm is discussed and theoretical results are verified by an application to a temperature control system in a heating plant. Four control algorithms are compared: PID and PI algorithms discretized with Euler and bilinear transformation, respectively. It is shown that for the heating plant used in this work, a bilinear PI algorithm implemented in a small 8-bit microcontroller outperforms a commercial controller from Panasonic. It is also demonstrated that all the derived algorithms can be implemented using integer calculations only, obviating the need for expensive and time-consuming floating-point calculations. This work bridges the gap between control theory equations and the implementation of control systems in small embedded systems with no inherent floating-point processing power.展开更多
动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清...动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清理动态障碍物(清障)问题却无人问津,相对应的多智能体清障算法更是屈指可数。为解决多智能体清障问题,文中提出了一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法(Multi-Agent Cooperative Algorithm for Obstacle Clearance Based on Deep Deterministic Policy Gradient and Attention Critic, MACOC)。首先,创建了首个多智能体协同清障的环境模型,定义了多智能体及动态障碍物的运动学模型,并根据智能体和动态障碍物数量的不同,构建了4种仿真实验环境;其次,将多智能体协同清障过程定义为马尔可夫决策过程(Markov Decision Process, MDP),构建了多智能体t的状态空间、动作空间和奖励函数;最后,提出一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法,并在多智能体协同清障仿真环境中与经典的多智能体强化学习算法进行对比。实验证明,相比对比算法,所提出的MACOC算法清障的成功率更高、速度更快,对复杂环境的适应性更好。展开更多
文摘Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation;adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.
基金Supported by National High Technology Research and Development Program of China(863 Program,Grant No.2015AA043701-02)
文摘When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system＇s dynamic behaviors because it involves multi-disciplinary subsystems. Currently, a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge： spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1） a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2） a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1） can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2） effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.
基金the National Natural Science Foundation of China(Grant No.594305050).
文摘On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the essential governing equations are presented. This work provides a new method to achieve the numerical solutions of the mechanic of finite deformation.
文摘This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Two different methods for discretizing the control expression are compared: Euler transformation and bilinear transformation. The sampling rate’s impact on the algorithm is discussed and theoretical results are verified by an application to a temperature control system in a heating plant. Four control algorithms are compared: PID and PI algorithms discretized with Euler and bilinear transformation, respectively. It is shown that for the heating plant used in this work, a bilinear PI algorithm implemented in a small 8-bit microcontroller outperforms a commercial controller from Panasonic. It is also demonstrated that all the derived algorithms can be implemented using integer calculations only, obviating the need for expensive and time-consuming floating-point calculations. This work bridges the gap between control theory equations and the implementation of control systems in small embedded systems with no inherent floating-point processing power.
文摘动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清理动态障碍物(清障)问题却无人问津,相对应的多智能体清障算法更是屈指可数。为解决多智能体清障问题,文中提出了一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法(Multi-Agent Cooperative Algorithm for Obstacle Clearance Based on Deep Deterministic Policy Gradient and Attention Critic, MACOC)。首先,创建了首个多智能体协同清障的环境模型,定义了多智能体及动态障碍物的运动学模型,并根据智能体和动态障碍物数量的不同,构建了4种仿真实验环境;其次,将多智能体协同清障过程定义为马尔可夫决策过程(Markov Decision Process, MDP),构建了多智能体t的状态空间、动作空间和奖励函数;最后,提出一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法,并在多智能体协同清障仿真环境中与经典的多智能体强化学习算法进行对比。实验证明,相比对比算法,所提出的MACOC算法清障的成功率更高、速度更快,对复杂环境的适应性更好。
