The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying L...The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.展开更多
A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed,which is simpler but more tailored than the shifting bottleneck(SB)procedure.In this algorithm,a schedule fo...A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed,which is simpler but more tailored than the shifting bottleneck(SB)procedure.In this algorithm,a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules.Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems.Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems.展开更多
This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than ...This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, with the fuzzy decision-making approach, the designer has more freedom in specifying the desired process behavior.展开更多
In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable t...In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.展开更多
文摘The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.
基金This project is supported by National Natural Science Foundation of China(No.60274013,No.60474002)Shanghai City Development Found for Science and Technology,China(No.04DZ11008)
文摘A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed,which is simpler but more tailored than the shifting bottleneck(SB)procedure.In this algorithm,a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules.Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems.Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems.
基金This project was supported by the National Nature Science Foundation of China (No. 60074004) andHebei Provincial Natural Scien
文摘This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, with the fuzzy decision-making approach, the designer has more freedom in specifying the desired process behavior.
基金This work was supported by the National Science Foundation of China(Grant 2014CB249200)the National Natural Science Foundation of China(Grant 61873162)the Shanghai Pujiang Program(Grant 18PJ1405500).
文摘In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.
