Linear-Quadratic Optimization on Finite Horizon

1997 ◽  
pp. 228-269
Author(s):  
Aristide Halanay ◽  
Judita Samuel
Keyword(s):  
Quadratic Optimization ◽  
Finite Horizon ◽  
Linear Quadratic

The Design of Optimal Lateral Motion Control of an UAV Using the Linear-Quadratic Optimization Method in the Complex Domain

2020 ◽  
Vol 13 (6) ◽  
pp. 217
Author(s):  
Vadim Kramar ◽  
Vasiliy Alchakov ◽  
Aleksey Kabanov ◽  
Sergey Dudnikov ◽  
Aleksandr Dmitriev
Keyword(s):  
Motion Control ◽  
Optimization Method ◽  
Quadratic Optimization ◽  
Complex Domain ◽  
Lateral Motion ◽  
Linear Quadratic

Constrained Linear Quadratic Optimization

2020 ◽  
pp. 13-37
Author(s):  
Jun Ma ◽  
Xiaocong Li ◽  
Kok Kiong Tan
Keyword(s):  
Quadratic Optimization ◽  
Linear Quadratic

scholarly journals Finite-Horizon Optimal Tracking Guidance for Aircraft Based on Approximate Dynamic Programming

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shizheng Wan ◽  
Xiaofei Chang ◽  
Quancheng Li ◽  
Jie Yan
Keyword(s):  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Least Squares Method ◽  
Flight Path ◽  
Finite Horizon ◽  
Dynamic Programming Method ◽  
Reference Trajectory ◽  
Linear Quadratic ◽  
Optimal Tracking ◽  
Flight Path Angle

Referring to the optimal tracking guidance of aircraft, the conventional time based kinematics model is transformed into a downrange based model by independent variable replacement. The deviations of in-flight altitude and flight path angle are penalized and corrected to achieve high precision tracking of reference trajectory. The tracking problem is solved as a linear quadratic regulator applying small perturbation theory, and the approximate dynamic programming method is used to cope with the solving of finite-horizon optimization. An actor-critic structure is established to approximate the optimal tracking controller and minimum cost function. The least squares method and Adam optimization algorithm are adopted to learn the parameters of critic network and actor network, respectively. A boosting trajectory with maximum final velocity is generated by Gauss pseudospectral method for the validation of guidance strategy. The results show that the trained feedback control parameters can effectively resist random wind disturbance, correct the initial altitude and flight path angle deviations, and achieve the goal of following a given trajectory.

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