In this paper, artificial neural network controller (NNC) was designed and used to adjust and control the work of the laser beam steering device consist of liquid crystals cell and other optical components. The target of the neurocontroller is to guarantee smooth deliver and exactly control the laser beam to a required location via control the liquid crystals cell and the optical components. Ba...

The neurocontroller of the pasteurization machine was developed. It consists of two parts: the neuroemulator and the serial neurocontroller, which are based on the multilayer perceptron structure. The neurocontroller has been implemented as a module in the SCADA-system at OJSC “Savushkin product” and has a good effect in comparison with the conventional PID controller. Testing showed the effect...

This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evo...

This paper presents a hardware-friendly approach for adapting the structure of a reinforcement, learning-based neurocontroller. An unsupervised clustering algorithm is used to partition the state space of a system and to adapt the size of its reinforcement module. In the well-known inverted pendulum problem, the system has proven to be much faster than previous neurocontroller approaches. We ar...

We here approach the problem of designing a controller for automatic rendezvous and docking (AR&D). As a first step towards a fully reactive neurocontroller (based on state feedback), an artificial neural network is trained offline by a fitness based genetic algorithm to fulfill the docking task. Its performance using one specific docking case, with fixed initial and boundary conditions, is com...

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neuro...

In this study, approximate dynamics programming framework is utilized for solving the Bellman equation related to the fixed-final-time optimal tracking problem of input-affine nonlinear systems. Convergence of the weights of the neurocontroller in the proposed successive approximation based algorithms is provided and the network is trained to provide the optimal solution to the problems with a)...

In this article, a modular neurocontroller is presented. It has the capability to generate a reactive behavior of walking machines. The neurocontroller is formed on the basis of a modular structure. It consists of the three different functionality modules: neural preprocessing, a neural oscillator network and velocity regulating networks. Neural preprocessing is for sensory signal processing. T...

This paper presents the design of an infinite horizon nonlinear optimal neurocontroller that replaces the conventional automatic voltage regulator and the turbine governor (CONVC) for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the novel optimization neuro-dynamic programming algorithm based on dual heuristic programming (DHP), whi...