Luffing mechanism is a key component of the construction machinery.This paper proposes a two degree of freedom(2-DOF)luffing mechanism,which has one more pair of driving cylinders than the single DOF luffing mechanism...Luffing mechanism is a key component of the construction machinery.This paper proposes a two degree of freedom(2-DOF)luffing mechanism,which has one more pair of driving cylinders than the single DOF luffing mechanism,to improve the performance of the machinery.To establish the dynamic model of the 2-DOF luffing mechanism,firstly,we develop a hierarchical method to deduce the Jacobian matrix and Hessian matrix for obtaining the kinematics equations.Subsequently,we divide the luffing mechanism into six bodies considering actuators,and deduce the kinetic equations of each body by the Newton-Euler method.Based on the dynamic model,we simulate the luffing process.Finally,a prototype is built on a pile driver to validate the model.Simulations and experiments show that the dynamic model can reflect the dynamic properties of the proposed luffing mechanism.And the control strategy that the front cylinders retract first shows better mechanical behavior than the other two control strategies.This research provides a reference for the design and application of 2-DOF luffing mechanism on construction machinery.The modeling approach can also be applied to similar mechanism with serial closed kinematic chains,which allows to calculate the dynamic parameters easily and exactly.展开更多
An inverse system method based optimal control strategy was proposed for the shunt hybrid active power filter （SHAPF） to enhance its harmonic elimination performance. Based on the inverse system method, the d-axis a...An inverse system method based optimal control strategy was proposed for the shunt hybrid active power filter （SHAPF） to enhance its harmonic elimination performance. Based on the inverse system method, the d-axis and q-axis current dynamics of the SHAPF system were decoupled and linearized into two pseudolinear subsystems. Then, an optimal feedback controUer was designed for the pseudolinear system, and the stability condition of the resulting zero dynamics was presented. Under the control strategy, the current dynamics can asymptotically converge to their reference states and the zero dynamics can be bounded. Simulation results show that the proposed control strategy is robust against load variations and system parameter mismatches, its steady-state performance is better than that of the traditional linear control strategy.展开更多
Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production de...Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.展开更多
基金Project(2015B020238014)supported by the Science and Technology Program of Guangdong Province,China。
文摘Luffing mechanism is a key component of the construction machinery.This paper proposes a two degree of freedom(2-DOF)luffing mechanism,which has one more pair of driving cylinders than the single DOF luffing mechanism,to improve the performance of the machinery.To establish the dynamic model of the 2-DOF luffing mechanism,firstly,we develop a hierarchical method to deduce the Jacobian matrix and Hessian matrix for obtaining the kinematics equations.Subsequently,we divide the luffing mechanism into six bodies considering actuators,and deduce the kinetic equations of each body by the Newton-Euler method.Based on the dynamic model,we simulate the luffing process.Finally,a prototype is built on a pile driver to validate the model.Simulations and experiments show that the dynamic model can reflect the dynamic properties of the proposed luffing mechanism.And the control strategy that the front cylinders retract first shows better mechanical behavior than the other two control strategies.This research provides a reference for the design and application of 2-DOF luffing mechanism on construction machinery.The modeling approach can also be applied to similar mechanism with serial closed kinematic chains,which allows to calculate the dynamic parameters easily and exactly.
基金Project(61174068)supported by the National Natural Science Foundation of China
文摘An inverse system method based optimal control strategy was proposed for the shunt hybrid active power filter （SHAPF） to enhance its harmonic elimination performance. Based on the inverse system method, the d-axis and q-axis current dynamics of the SHAPF system were decoupled and linearized into two pseudolinear subsystems. Then, an optimal feedback controUer was designed for the pseudolinear system, and the stability condition of the resulting zero dynamics was presented. Under the control strategy, the current dynamics can asymptotically converge to their reference states and the zero dynamics can be bounded. Simulation results show that the proposed control strategy is robust against load variations and system parameter mismatches, its steady-state performance is better than that of the traditional linear control strategy.
基金supported by the National Natural Science Foundation of China (Grant No. 61004095F030202)the China Important National Sci-ence & Technology Specific Projects (Grant No. 2008ZX05030-05-002)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No. 09CX05007A)the National Basic Research Program of China (Grant No. 2011CB201000)
文摘Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.
