For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve...For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival （TOA） sequences are subtracted. And final y the radial ve-locity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is bet-ter than the arithmetic average of the Cramer-Rao lower bound （CRLB） for monopulse under suitable conditions.展开更多
The time difference of arrival(TDOA)estimation plays a crucial role in the accurate localization of the satellite interference source.In the dual-satellites interference source localization system,the target signal fr...The time difference of arrival(TDOA)estimation plays a crucial role in the accurate localization of the satellite interference source.In the dual-satellites interference source localization system,the target signal from the adjacent satellite is likely to be interfered by the normal communication signal with the same frequency.Therefore,the signal to noise ratio(SNR)of the target signal would become too low,and the TDOA estimation through cross-correlation processing would be unreliable or even unattainable.This paper proposes a technique based on blind separation to solve the co-channel interference problem,where separation of the mixed signal can be carried out by the particle filter(PF)algorithm.The experimental results show that the proposed method could achieve more accurate TDOA estimation.The measured data obtained by using the software radio platform at 915 MHz and 2 GHz respectively verify the effectiveness of the proposed method.展开更多
Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion pl...Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.展开更多
A sliding mode and active disturbance rejection control(SM-ADRC)was employed to regulate the speed of a permanent magnet synchronous motor(PMSM).The major advantages of the proposed control scheme are that it can main...A sliding mode and active disturbance rejection control(SM-ADRC)was employed to regulate the speed of a permanent magnet synchronous motor(PMSM).The major advantages of the proposed control scheme are that it can maintain the original features of ADRC and make the parameters of ADRC transition smoothly.The proposed control scheme also ensures speed control accuracy and improves the robustness and anti-load disturbance ability of the system.Moreover,through the analysis of a d-axis current output equation,a novel current-loop SM-ADRC is presented to improve the system’s dynamic performance and inner ability of anti-load disturbance.Results of a simulation and experiments show that the improved sliding-mode ADRC system has the advantages of fast response,small overshoot,small steady-state error,wide speed range and high control accuracy.It shows that the system has strong anti-interference ability to reduce the influence of variations in rotational inertia,load and internal parameters.展开更多
A new cascade control program was proposed based on modified internal model control to handle stable,unstable and integrating processes with time delay.The program had totally four controllers of which the secondary l...A new cascade control program was proposed based on modified internal model control to handle stable,unstable and integrating processes with time delay.The program had totally four controllers of which the secondary loop had two controllers and the primary loop had two controllers.The two secondary loop controllers were designed using IMC technique.They were decoupled completely and could be adjusted independently,which avoided the undesirable influence on performance of the primary controllers.The main controller in the primary loop was devised as a PID using the method of minimum sensitivity,which could guarantee not only the nominal performance but also the robust stability of the system.A setpoint filter was added in the primary loop to improve the tracking performance.All the controllers of the two closed-loops were designed analytically,and could be adjusted and optimized by single parameter respectively.Simulations were carried out on three various processes with time delay,and the results show that the proposed method can provide a better performance of both set-point tracking and disturbance rejection and robustness against parameters perturbation.展开更多
The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual incom...The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
Due to the frequency of occlusion, cluttering and lowcontrast edges, gray intensity based active contour models oftenfail to segment meaningful objects. Prior shape information is usuallyutilized to segment desirable ...Due to the frequency of occlusion, cluttering and lowcontrast edges, gray intensity based active contour models oftenfail to segment meaningful objects. Prior shape information is usuallyutilized to segment desirable objects. A parametric shape priormodel is proposed. Firstly, principal component analysis is employedto train object shape and transformation is added to shaperepresentation. Then the energy function is constructed througha combination of shape prior energy, gray intensity energy andshape constraint energy of the kernel density function. The objectboundary extraction process is converted into the parameters solvingprocess of object shape. Besides, two new shape prior energyfunctions are defined when desirable objects are occluded by otherobjects or some parts of them are missing. Finally, an alternatingdecent iteration solving scheme is proposed for numerical implementation.Experiments on synthetic and real images demonstratethe robustness and accuracy of the proposed method.展开更多
Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issu...Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.展开更多
基金supported by the National Natural Science Foundationof China(61201208)
文摘For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival （TOA） sequences are subtracted. And final y the radial ve-locity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is bet-ter than the arithmetic average of the Cramer-Rao lower bound （CRLB） for monopulse under suitable conditions.
基金supported by the Fundamental Research Funds for the Central Universities(2082604194194)
文摘The time difference of arrival(TDOA)estimation plays a crucial role in the accurate localization of the satellite interference source.In the dual-satellites interference source localization system,the target signal from the adjacent satellite is likely to be interfered by the normal communication signal with the same frequency.Therefore,the signal to noise ratio(SNR)of the target signal would become too low,and the TDOA estimation through cross-correlation processing would be unreliable or even unattainable.This paper proposes a technique based on blind separation to solve the co-channel interference problem,where separation of the mixed signal can be carried out by the particle filter(PF)algorithm.The experimental results show that the proposed method could achieve more accurate TDOA estimation.The measured data obtained by using the software radio platform at 915 MHz and 2 GHz respectively verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China (62173251)the“Zhishan”Scholars Programs of Southeast University+1 种基金the Fundamental Research Funds for the Central UniversitiesShanghai Gaofeng&Gaoyuan Project for University Academic Program Development (22120210022)
文摘Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.
基金Project(2011AA11A10102) supported by the High-tech Research and Development Program of China
文摘A sliding mode and active disturbance rejection control(SM-ADRC)was employed to regulate the speed of a permanent magnet synchronous motor(PMSM).The major advantages of the proposed control scheme are that it can maintain the original features of ADRC and make the parameters of ADRC transition smoothly.The proposed control scheme also ensures speed control accuracy and improves the robustness and anti-load disturbance ability of the system.Moreover,through the analysis of a d-axis current output equation,a novel current-loop SM-ADRC is presented to improve the system’s dynamic performance and inner ability of anti-load disturbance.Results of a simulation and experiments show that the improved sliding-mode ADRC system has the advantages of fast response,small overshoot,small steady-state error,wide speed range and high control accuracy.It shows that the system has strong anti-interference ability to reduce the influence of variations in rotational inertia,load and internal parameters.
基金Project(J11LG02) supported by the Science and Technology Funds of Education Department of Shandong Province,China
文摘A new cascade control program was proposed based on modified internal model control to handle stable,unstable and integrating processes with time delay.The program had totally four controllers of which the secondary loop had two controllers and the primary loop had two controllers.The two secondary loop controllers were designed using IMC technique.They were decoupled completely and could be adjusted independently,which avoided the undesirable influence on performance of the primary controllers.The main controller in the primary loop was devised as a PID using the method of minimum sensitivity,which could guarantee not only the nominal performance but also the robust stability of the system.A setpoint filter was added in the primary loop to improve the tracking performance.All the controllers of the two closed-loops were designed analytically,and could be adjusted and optimized by single parameter respectively.Simulations were carried out on three various processes with time delay,and the results show that the proposed method can provide a better performance of both set-point tracking and disturbance rejection and robustness against parameters perturbation.
基金supported by the National Natural Science Foundation of China(61503407,61806219,61703426,61876189,61703412)the China Postdoctoral Science Foundation(2016 M602996)。
文摘The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
基金supported by the National Natural Science Foundation of China(6137214261571005U1401252)
文摘Due to the frequency of occlusion, cluttering and lowcontrast edges, gray intensity based active contour models oftenfail to segment meaningful objects. Prior shape information is usuallyutilized to segment desirable objects. A parametric shape priormodel is proposed. Firstly, principal component analysis is employedto train object shape and transformation is added to shaperepresentation. Then the energy function is constructed througha combination of shape prior energy, gray intensity energy andshape constraint energy of the kernel density function. The objectboundary extraction process is converted into the parameters solvingprocess of object shape. Besides, two new shape prior energyfunctions are defined when desirable objects are occluded by otherobjects or some parts of them are missing. Finally, an alternatingdecent iteration solving scheme is proposed for numerical implementation.Experiments on synthetic and real images demonstratethe robustness and accuracy of the proposed method.
基金supported by the Shenzhen Innovation Technology Program(JCYJ20160422112909302)
文摘Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.
