
The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem.
FOS: Computer and information sciences, software package, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Chemical technology, TP1-1185, Systems and Control (eess.SY), adaptive dynamic programming, Electrical Engineering and Systems Science - Systems and Control, Article, Feedback, Machine Learning (cs.LG), optimal control, Computer Science - Robotics, Artificial Intelligence (cs.AI), Nonlinear Dynamics, Optimization and Control (math.OC), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Neural Networks, Computer, Mathematics - Optimization and Control, Robotics (cs.RO), Software
FOS: Computer and information sciences, software package, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Chemical technology, TP1-1185, Systems and Control (eess.SY), adaptive dynamic programming, Electrical Engineering and Systems Science - Systems and Control, Article, Feedback, Machine Learning (cs.LG), optimal control, Computer Science - Robotics, Artificial Intelligence (cs.AI), Nonlinear Dynamics, Optimization and Control (math.OC), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Neural Networks, Computer, Mathematics - Optimization and Control, Robotics (cs.RO), Software
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