Optimal Control Data Research Articles

Data-Mining-Based Hardware-Efficient Neural Network Controller for DC–DC Switching Converters

For the exiting neural network (NN)-based controllers, since the training data are not optimized using different control algorithms, their controlling performance improvements are limited. This paper proposes a data-mining-based hardware-efficient NN controller for DC–DC switching converters. Firstly, the data mining strategy for NN controllers is proposed to optimize the training data. In data mining process, the optimal control data are selected from different control algorithms to create an optimized dataset for training NN controller. In addition, to reduce the hardware resources, the structure of a hardware-efficient NN controller is proposed, which comprises coarse-tuning NN (NN-ct) and fine-tuning NN (NN-ft) controllers, and the switching between two NN controllers is conducted based on the output voltage variation. The NN-ct and NN-ft controllers are trained using the optimized fine-tuning and coarse-tuning sub-datasets, respectively. By switching the weights and biases and disabling some hidden-layer nodes, two separate NN controllers can share some hardware resources, thus reducing the hardware cost without sacrificing the performance. The simulation and experimental results demonstrate that data-mining-based NN controller has better performance than other NN controllers. Furthermore, the hardware resource requirement of a hardware-efficient NN controller can be reduced by at least 17% when compared with an ordinary NN controller.

Optimal control data assimilation with an atmospheric model

The problem of time-continuous meteorological data assimilation is addressed. An optimal control method is proposed and studied for a simple well-posed 2D atmospheric model and continuous in time and space observations. An existence result for the optimal control problem is given. Then the first order necessary conditions of optimality are explicited using a forced version of the linearized adjoint model; allowing the use of a classical descent method to solve the problem. The reliability of the method is shown on some numerical tests. Finally, the existence and characterization results of an optimal control are extended to the case of observations distributed in time.

Feedback control of minimum‐time optimal control problems using neural networks

AbstractThis paper presents an optimal feedback controller capable of driving a non‐linear control system from an arbitrary initial state to a fixed final state in minimum time. The controller is based on a feedforward multilayer neural network trained repeatedly using open‐loop optimal control data which densely span the field of extremals of the non‐linear system. The effectiveness of the controller is clearly demonstrated by a simulation on a two‐link robot manipulator. The effect of sensor/actuator noise and parameter variation is also included to confirm the robustness of the controller.