Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and thos...Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and those with high dynamic performance typically have low energy efficiency. In this paper, the variants of secondary control（VSC） with power recovery are developed to solve this problem for loading hydraulic driving devices that operate under variable pressure, unlike classical secondary control（CSC） that operates in constant pressure network. Hydrostatic secondary control units are used as the loading components, by which the absorbed mechanical power from the tested device is converted into hydraulic power and then fed back into the tested system through 4 types of feedback passages（FPs）. The loading subsystem can operate in constant pressure network, controlled variable pressure network, or the same variable pressure network as that of the tested device by using different FPs. The 4 types of systems are defined, and their key techniques are analyzed, including work principle, simulating the work state of original tested device, static operation points, loading performance, energy efficiency, and control strategy, etc. The important technical merits of the 4 schemes are compared, and 3 of the schemes are selected, designed, simulated using AMESim and evaluated. The researching results show that the investigated systems can simulate the given loads effectively, realize the work conditions of the tested device, and furthermore attain a high power recovery efficiency that ranges from 0.54 to 0.85, even though the 3 schemes have different loading performances and energy efficiencies. This paper proposes several loading schemes that can achieve both high dynamic performance and high power recovery efficiency.展开更多
Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control e...Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.展开更多
During dynamic walking of biped robots, the underactuated rotating degree of freedom （DOF） emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant. This paper addres...During dynamic walking of biped robots, the underactuated rotating degree of freedom （DOF） emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant. This paper addresses the asymptotic orbit stability for dimension-variant hybrid systems （DVHS）. Based on the generalized Poincare map, the stability criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with fiat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.展开更多
By using the precise integration method, the numerical solution of linear quadratic Gaussian (LQG) optimal control problem was discussed. Based on the separation principle, the LQG central problem decomposes, or separ...By using the precise integration method, the numerical solution of linear quadratic Gaussian (LQG) optimal control problem was discussed. Based on the separation principle, the LQG central problem decomposes, or separates, into an optimal state-feedback control problem and an optimal state estimation problem. That is the off-line solution of two sets of Riccati differential equations and the on-line integration solution of the state vector from a set of time-variant differential equations. The present algorithms are not only appropriate to solve the two-point boundary-value problem and the corresponding Riccati differential equation, but also can be used to solve the estimated state from the time-variant differential equations. The high precision of precise integration is of advantage for the control and estimation. Numerical examples demonstrate the high precision and effectiveness of the algorithm.展开更多
在大规模分布式能源(distributed generation,DG)接入的环境下,实际配电侧负荷需求的实时剧烈变动,传统配电网侧静态的重构与调度方法已经不能适应基于信息技术与电力电子技术的主动配电网(active distribution network,AND)的发展。为...在大规模分布式能源(distributed generation,DG)接入的环境下,实际配电侧负荷需求的实时剧烈变动,传统配电网侧静态的重构与调度方法已经不能适应基于信息技术与电力电子技术的主动配电网(active distribution network,AND)的发展。为此,文章提出一种考虑负荷需求的时变特性和配电公司的运行经济效益的网架快速重构和DG快速调控新策略。基于模拟3类不同用电客户在时间与空间上的负荷需求模式,建立了考虑配电公司电力市场购电成本、快速重构成本和DG运行成本的运行经济效益分析模型,并使用烟花优化算法进行求解。为验证所述模型和方法的可行性,设立了6种配电网运行场景进行相关分析,仿真算例表明,通过充分利用ADN快速重构与DG调控手段,可以降低配电公司的运行成本,引导配电公司实现更多的新能源消纳。展开更多
文摘Current high power load simulators are generally incapable of obtaining both high loading performance and high energy efficiency. Simulators with high energy efficiency are used to simulate static-state load, and those with high dynamic performance typically have low energy efficiency. In this paper, the variants of secondary control（VSC） with power recovery are developed to solve this problem for loading hydraulic driving devices that operate under variable pressure, unlike classical secondary control（CSC） that operates in constant pressure network. Hydrostatic secondary control units are used as the loading components, by which the absorbed mechanical power from the tested device is converted into hydraulic power and then fed back into the tested system through 4 types of feedback passages（FPs）. The loading subsystem can operate in constant pressure network, controlled variable pressure network, or the same variable pressure network as that of the tested device by using different FPs. The 4 types of systems are defined, and their key techniques are analyzed, including work principle, simulating the work state of original tested device, static operation points, loading performance, energy efficiency, and control strategy, etc. The important technical merits of the 4 schemes are compared, and 3 of the schemes are selected, designed, simulated using AMESim and evaluated. The researching results show that the investigated systems can simulate the given loads effectively, realize the work conditions of the tested device, and furthermore attain a high power recovery efficiency that ranges from 0.54 to 0.85, even though the 3 schemes have different loading performances and energy efficiencies. This paper proposes several loading schemes that can achieve both high dynamic performance and high power recovery efficiency.
基金supported by Specialized Research Fund for Doctoral Program of Higher Education of China (Grant No. 20091102120038)
文摘Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.
基金the National Natural Science Foundation of China (No. 50575119)the 863 Program(No. 2006AA04Z253)the Ph.D.Programs Foundation of Ministry of Education of China(No. 20060003026)
文摘During dynamic walking of biped robots, the underactuated rotating degree of freedom （DOF） emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant. This paper addresses the asymptotic orbit stability for dimension-variant hybrid systems （DVHS）. Based on the generalized Poincare map, the stability criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with fiat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.
文摘By using the precise integration method, the numerical solution of linear quadratic Gaussian (LQG) optimal control problem was discussed. Based on the separation principle, the LQG central problem decomposes, or separates, into an optimal state-feedback control problem and an optimal state estimation problem. That is the off-line solution of two sets of Riccati differential equations and the on-line integration solution of the state vector from a set of time-variant differential equations. The present algorithms are not only appropriate to solve the two-point boundary-value problem and the corresponding Riccati differential equation, but also can be used to solve the estimated state from the time-variant differential equations. The high precision of precise integration is of advantage for the control and estimation. Numerical examples demonstrate the high precision and effectiveness of the algorithm.
文摘在大规模分布式能源(distributed generation,DG)接入的环境下,实际配电侧负荷需求的实时剧烈变动,传统配电网侧静态的重构与调度方法已经不能适应基于信息技术与电力电子技术的主动配电网(active distribution network,AND)的发展。为此,文章提出一种考虑负荷需求的时变特性和配电公司的运行经济效益的网架快速重构和DG快速调控新策略。基于模拟3类不同用电客户在时间与空间上的负荷需求模式,建立了考虑配电公司电力市场购电成本、快速重构成本和DG运行成本的运行经济效益分析模型,并使用烟花优化算法进行求解。为验证所述模型和方法的可行性,设立了6种配电网运行场景进行相关分析,仿真算例表明,通过充分利用ADN快速重构与DG调控手段,可以降低配电公司的运行成本,引导配电公司实现更多的新能源消纳。
