Online and hard constrained adaptive dynamic programming algorithm for energy storage control in smart buildings

2021 ◽  
Author(s):  
Zitao Chen ◽  
Si‐zhe Chen ◽  
Yun Zhang ◽  
Quanbin Deng ◽  
Xianxian Zeng
Keyword(s):  
Dynamic Programming ◽  
Energy Storage ◽  
Dynamic Programming Algorithm ◽  
Adaptive Dynamic Programming ◽  
Programming Algorithm ◽  
Smart Buildings ◽  
Adaptive Dynamic

Ascent trajectory tracking method using time-varying quadratic adaptive dynamic programming

2018 ◽  
Vol 233 (11) ◽  
pp. 4154-4165
Author(s):  
Zhengxiang Sun ◽  
Tao Chao ◽  
Songyan Wang ◽  
Ming Yang
Keyword(s):  
Dynamic Programming ◽  
Trajectory Tracking ◽  
Control Variable ◽  
Dynamic Programming Algorithm ◽  
Adaptive Dynamic Programming ◽  
Programming Algorithm ◽  
System Control ◽  
Time Varying ◽  
Nonlinear Method ◽  
Adaptive Dynamic

Ascent trajectory tracking in the longitudinal plane is a class of nonaffine noncascade nonlinear system control problems with single input and multiple output, which is difficult to control by nonlinear method directly. Adaptive dynamic programming algorithm has the advantages of precise control and adaptability for general nonlinear control problem, and the time-varying quadratic adaptive dynamic programming algorithm proposed in this paper promotes the convergence and calculation speed of the traditional adaptive dynamic programming algorithm. To implement the algorithm effectively, the independent variables of ascent model are substituted in the launching coordinate, and then the model is treated as a discrete nominal trajectory tracking problem. Besides, the heuristic dynamic programming structure is used to train the processed model, and thus only the time-varying weight in the designed evaluation network needs to be updated. Simulation shows that the proposed algorithm can update the control variable online after predicting the cost function offline with the dynamic equations, which is faster than the general adaptive dynamic programming algorithm. In addition, the proposed algorithm can effectively and accurately track the nominal trajectory under the uncertainty of parameters compared with the linear quadratic regulators algorithm.

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