A novel approach to adaptive direct neurocontrol is discussed in this paper. The objective is to construct an adaptive control scheme for unknown time-dependent nonlinear plants without using a model of the plant. The proposed approach is neural-network based and combines the self-tuning principle with reinforcement learning. The control scheme consists of a controller, a utility estimator, an exploration module, a learning module and a rewarding module. The controller and the utility estimator are implemented together in a single radial basis function network. The learning method involves structural adaptation (growing neural network) and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration–exploitation dilemma is solved through smooth transitions between the two modes. This enables rapid exploration response to novel plant dynamics and stable operation in the absence of changes in plant dynamics. The controller is capable of asymptotically approaching the desired reference trajectory, which is demonstrated in a simulation study.
