A new coordination scheme for multi-robot systems is proposed. A state space model of the multi- robot system is defined and constructed in which the system＇s initial and goal states are included along with the task ...A new coordination scheme for multi-robot systems is proposed. A state space model of the multi- robot system is defined and constructed in which the system＇s initial and goal states are included along with the task definition and the system＇s internal and external constraints. Task accomplishment is considered a transition of the system state in its state space （SS） under the system＇s constraints. Therefore, if there exists a connectable path within reachable area of the SS from the initial state to the goal state, the task is realizable. The optimal strategy for the task realization under constraints is investigated and reached by searching for the optimal state transition trajectory of the robot system in the SS. Moreover, if there is no connectable path, which means the task cannot be performed Successfully, the task could be transformed to be realizable by making the initial state and the goal state connectable and finding a path connecting them in the system＇s SS. This might be done via adjusting the system＇s configuration and/or task constraints. Experiments of multi-robot formation control with obstacles in the environment are conducted and simulation results show the validity of the proposed method.展开更多
Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF...Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.展开更多
This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected ...This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.展开更多
This paper proposes a new coordination method for multi-robot system.The state space for a multi-robot system is constructed according to the task requirements and system characteristics.Reachable statefor the system ...This paper proposes a new coordination method for multi-robot system.The state space for a multi-robot system is constructed according to the task requirements and system characteristics.Reachable statefor the system is constrained by the system s internal and external constraints,under which the task isexecutable if there exists a state transition trajectory from the initial to the goal state in its state space.Ifthe task is realizable,the feasible or the optimal strategy for task execution could then be investigated inthe state space.Otherwise,the task could be modified to be realizable via adjusting system s configura-tions and/or task constraints,which provides critical guidance for system reconstructions.This con-tributes to the designing and planning of the robotic tasks.Experiments of multi-robot formation movementare conducted to show the validity of the proposed method.展开更多
A new ball screw dynamic model was developed under the adequate consideration of the interaction in the screw-nut assembly (not only the mutual-coupling factors but also the self-coupling factors) . Based on this mode...A new ball screw dynamic model was developed under the adequate consideration of the interaction in the screw-nut assembly (not only the mutual-coupling factors but also the self-coupling factors) . Based on this model,the multi-flexible body (MFB)dynamic model of ball screw feed drive system was then founded in order to take full account of the influencing factor of system flexibility and study the dynamic behaviors of the whole mechanical transmissions. Moreover,the MFB based state space modeling was proposed by modal state space method, which extraced the eigenmodes of more dominant modes and applied them into an MFB state space model,and realized the integrated model of servo drives and MFB mechanical transmissions more effectively and efficiently. In conclusion,the comparisons between simulations and experimental results show: the stiffness formulation of the ball screw assembly derived above is a suitable method for achieving accurate MFB models of ball screw mechanical transmission systems,this proposed MFB model is valid,and the integrated model of ball screw feed drive system is accurate and reliable. All these provide the important approaches and guidelines for dynamic characteristic study and selection of control parameters in the machine tool design period.展开更多
In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements ...In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.展开更多
基金the National Natural Science Foundation of China (60428303).
文摘A new coordination scheme for multi-robot systems is proposed. A state space model of the multi- robot system is defined and constructed in which the system＇s initial and goal states are included along with the task definition and the system＇s internal and external constraints. Task accomplishment is considered a transition of the system state in its state space （SS） under the system＇s constraints. Therefore, if there exists a connectable path within reachable area of the SS from the initial state to the goal state, the task is realizable. The optimal strategy for the task realization under constraints is investigated and reached by searching for the optimal state transition trajectory of the robot system in the SS. Moreover, if there is no connectable path, which means the task cannot be performed Successfully, the task could be transformed to be realizable by making the initial state and the goal state connectable and finding a path connecting them in the system＇s SS. This might be done via adjusting the system＇s configuration and/or task constraints. Experiments of multi-robot formation control with obstacles in the environment are conducted and simulation results show the validity of the proposed method.
基金supported by the National Natural Science Foundation of China(7092100160574058)+1 种基金the Key International Cooperation Programs of Hunan Provincial Science & Technology Department (2009WK2009)the General Program of Hunan Provincial Education Department(11C0023)
文摘Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
文摘This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.
基金Supported by the National Natural Science Foundation for Distinguished Young Scholars Abroad (No. 60428303)
文摘This paper proposes a new coordination method for multi-robot system.The state space for a multi-robot system is constructed according to the task requirements and system characteristics.Reachable statefor the system is constrained by the system s internal and external constraints,under which the task isexecutable if there exists a state transition trajectory from the initial to the goal state in its state space.Ifthe task is realizable,the feasible or the optimal strategy for task execution could then be investigated inthe state space.Otherwise,the task could be modified to be realizable via adjusting system s configura-tions and/or task constraints,which provides critical guidance for system reconstructions.This con-tributes to the designing and planning of the robotic tasks.Experiments of multi-robot formation movementare conducted to show the validity of the proposed method.
基金National Science and Technology Major Project of China(No.2011ZX04016-02)
文摘A new ball screw dynamic model was developed under the adequate consideration of the interaction in the screw-nut assembly (not only the mutual-coupling factors but also the self-coupling factors) . Based on this model,the multi-flexible body (MFB)dynamic model of ball screw feed drive system was then founded in order to take full account of the influencing factor of system flexibility and study the dynamic behaviors of the whole mechanical transmissions. Moreover,the MFB based state space modeling was proposed by modal state space method, which extraced the eigenmodes of more dominant modes and applied them into an MFB state space model,and realized the integrated model of servo drives and MFB mechanical transmissions more effectively and efficiently. In conclusion,the comparisons between simulations and experimental results show: the stiffness formulation of the ball screw assembly derived above is a suitable method for achieving accurate MFB models of ball screw mechanical transmission systems,this proposed MFB model is valid,and the integrated model of ball screw feed drive system is accurate and reliable. All these provide the important approaches and guidelines for dynamic characteristic study and selection of control parameters in the machine tool design period.
文摘In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.
