A penalty function method for exploratory adaptive-critic neural network control

A constrained penalty function method for exploratory adaptive-critic neural network (NN) control is presented. While constrained approximate dynamic programming has been effective to guarantee closed-loop system performance and stability objectives, in the presence of a change in the plant dynamics it may not have the necessary plasticity to explore and fully adapt to the new behaviors of the plant, if these violate the constraints. A generalized constrained approach is introduced to overcome these limitations. Through this methodology it is shown that NNs are not only capable to acquire new plasticity when necessary, but also can adjust their parametric structure reducing their hidden nodes and becoming more computationally efficient.