In this paper,a fast approach to generate time optimal and smooth trajectory has been developed and tested.Minimum time is critical for the productivity in industrial applications.Meanwhile,smooth trajectories based o...In this paper,a fast approach to generate time optimal and smooth trajectory has been developed and tested.Minimum time is critical for the productivity in industrial applications.Meanwhile,smooth trajectories based on cubic splines are desirable for their ability to limit vibrations and ensure the continuity of position,velocity and acceleration during the robot movement.The main feature of the approach is a satisfactory solution that can be obtained by a local modification process among each interval between two consecutive via-points.An analytical formulation simplifies the approach to smooth trajectory and few iterations are enough to determine the correct values.The approach can be applied in many robot manipulators which require high performance on time and smooth.The simulation and application of the approach on a palletizer robot are performed,and the experimental results provide evidence that the approach can realize the robot manipulators more efficiency and high smooth performance.展开更多
The paper analyses time series that exhibit equilibrium states. It analyses the formation of equilibrium and how the system can return to the aforementioned equilibrium. The tool that is used in the aforementioned ana...The paper analyses time series that exhibit equilibrium states. It analyses the formation of equilibrium and how the system can return to the aforementioned equilibrium. The tool that is used in the aforementioned analysis is time optimal control in the phase plane. It is proved that equilibrium state is sustainable if initial state is not too far from the equilibrium as well as control vector is large enough. On the other hand, if initial state is one standard deviation away from equilibrium state, it is proved that equilibrium cannot be reached. It is the same case with control vector. If it is unbounded, time optimal control cannot be applied. The approach that is introduced represents unconventional method of analysing equilibrium in time series.展开更多
This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined ...This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.展开更多
A dynamic parallel forecasting model is proposed,which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is i...A dynamic parallel forecasting model is proposed,which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly,the input data sets are optimized and their coherence is ensured,the region scale algorithm is modified and non-isometric multiscale region fuzzy time series model is built. At the same time,the particle swarm optimization algorithm about the particle speed,location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.展开更多
The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with dive...The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.展开更多
A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid-or graph-based modelin...A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid-or graph-based modeling is performed and the environment is divided into a set of grids or nodes. Then two time-based features of time interval and time cost are presented. The time intervals for each grid are built, during each interval the condition of the grid remains stable, and a time cost of passing through the grid is defined and assigned to each interval. Furthermore, the weight is introduced for taking both time and distance into consideration, and thus a sequence of multiscale paths with total time cost can be achieved. Experimental results show that the proposed method can handle the complex dynamic environment, obtain the global time optimal path and has the potential to be applied to the autonomous robot navigation and traffic environment.展开更多
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) ...This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.展开更多
Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. Fi...Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.展开更多
It is well known that the sufficient family of time-optimal paths for both Dubins' as well as Reeds-Shepp' s car models consist of the concatenation of circular arcs with maxmum curvature and straight line seg...It is well known that the sufficient family of time-optimal paths for both Dubins' as well as Reeds-Shepp' s car models consist of the concatenation of circular arcs with maxmum curvature and straight line segments, all tangentially connected.These time-optimal solutions suffer from some drawbacks. Their discontinuous curvature profde, together with the wear and impairment on the control equipment that the bang-bang solutions induce, calls for 'smoother' and more supple reference paths to follow. Avoiding the bang-bang solutions also raises the robustness with respect to any possible uncertainties. In this paper, our main tool for generating these 'nearly time-optimal', but nevertheless continuous-curvature paths, is to use the Pontryagin Maximum Principle (PMP) and make an appropriate and cunning choice of the Lagrangian function. Despite some rewarding simuhtion results, this concept tums out to be numerically divergent at some instances. Upon a more careful investigation, it can be concluded that the problem at hand is nearly singular. This is seen by applying the PMP to Dubins' car and studying the corresponding two point boundary value problem, which turn out to be singuhr. Realizing this, one is able to contradict the widespread belief that all the information about the motion of a mobile platform lies in the initial values of the auxiliary variables associated with the PMP.展开更多
We put forward a chaotic estimating model. by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by u...We put forward a chaotic estimating model. by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly , and the result of it is better than the method of statistics and other series means.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟...针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。展开更多
文摘In this paper,a fast approach to generate time optimal and smooth trajectory has been developed and tested.Minimum time is critical for the productivity in industrial applications.Meanwhile,smooth trajectories based on cubic splines are desirable for their ability to limit vibrations and ensure the continuity of position,velocity and acceleration during the robot movement.The main feature of the approach is a satisfactory solution that can be obtained by a local modification process among each interval between two consecutive via-points.An analytical formulation simplifies the approach to smooth trajectory and few iterations are enough to determine the correct values.The approach can be applied in many robot manipulators which require high performance on time and smooth.The simulation and application of the approach on a palletizer robot are performed,and the experimental results provide evidence that the approach can realize the robot manipulators more efficiency and high smooth performance.
文摘The paper analyses time series that exhibit equilibrium states. It analyses the formation of equilibrium and how the system can return to the aforementioned equilibrium. The tool that is used in the aforementioned analysis is time optimal control in the phase plane. It is proved that equilibrium state is sustainable if initial state is not too far from the equilibrium as well as control vector is large enough. On the other hand, if initial state is one standard deviation away from equilibrium state, it is proved that equilibrium cannot be reached. It is the same case with control vector. If it is unbounded, time optimal control cannot be applied. The approach that is introduced represents unconventional method of analysing equilibrium in time series.
基金Fund of Taishan Scholar in Shandong Province,Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.
基金supported by the National Defense Preliminary Research Program of China(A157167)the National Defense Fundamental of China(9140A19030314JB35275)
文摘A dynamic parallel forecasting model is proposed,which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly,the input data sets are optimized and their coherence is ensured,the region scale algorithm is modified and non-isometric multiscale region fuzzy time series model is built. At the same time,the particle swarm optimization algorithm about the particle speed,location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.
基金supported by the National Key R&D Program of China(2018AAA0101203)the National Natural Science Foundation of China(61673403,71601191)the JSPS KAKENHI(JP17K12751)。
文摘The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.
基金Supported by the National Natural Science Foundation of China(No.61100143,No.61370128)the Program for New Century Excellent Talents in University of the Ministry of Education of China(NCET-13-0659)Beijing Higher Education Young Elite Teacher Project(YETP0583)
文摘A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid-or graph-based modeling is performed and the environment is divided into a set of grids or nodes. Then two time-based features of time interval and time cost are presented. The time intervals for each grid are built, during each interval the condition of the grid remains stable, and a time cost of passing through the grid is defined and assigned to each interval. Furthermore, the weight is introduced for taking both time and distance into consideration, and thus a sequence of multiscale paths with total time cost can be achieved. Experimental results show that the proposed method can handle the complex dynamic environment, obtain the global time optimal path and has the potential to be applied to the autonomous robot navigation and traffic environment.
基金Project (Nos. 60174009 and 70071017) supported by the National
Natural Science Foundation of China
文摘This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
文摘Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.
文摘It is well known that the sufficient family of time-optimal paths for both Dubins' as well as Reeds-Shepp' s car models consist of the concatenation of circular arcs with maxmum curvature and straight line segments, all tangentially connected.These time-optimal solutions suffer from some drawbacks. Their discontinuous curvature profde, together with the wear and impairment on the control equipment that the bang-bang solutions induce, calls for 'smoother' and more supple reference paths to follow. Avoiding the bang-bang solutions also raises the robustness with respect to any possible uncertainties. In this paper, our main tool for generating these 'nearly time-optimal', but nevertheless continuous-curvature paths, is to use the Pontryagin Maximum Principle (PMP) and make an appropriate and cunning choice of the Lagrangian function. Despite some rewarding simuhtion results, this concept tums out to be numerically divergent at some instances. Upon a more careful investigation, it can be concluded that the problem at hand is nearly singular. This is seen by applying the PMP to Dubins' car and studying the corresponding two point boundary value problem, which turn out to be singuhr. Realizing this, one is able to contradict the widespread belief that all the information about the motion of a mobile platform lies in the initial values of the auxiliary variables associated with the PMP.
文摘We put forward a chaotic estimating model. by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly , and the result of it is better than the method of statistics and other series means.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。
