A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior ofa four-legged and a sixlegged walking machine. The controller utilizes discrete-time neurodynamics, and is ofmodular structure. One module isfor processing sensor signals, one is a neural oscillator network serving as a central pattern generator, and the third one is a so-called velocit...

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. This is achieved by integrating the Error Backpropagation (EBP) technique with Variable Structure Systems (VSS) methodology, which is well...

We discuss a variety of adaptive critic designs (ACD’s) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: Heuristic dynamic programming (HDP), dual heu...

Previous work has provided the theoretical foundations of a constructive design procedure for uniform approximation of smooth functions to a chosen degree of accuracy using networks of gaus-sian radial basis functions. This construction and the guaranteed uniform bounds were then shown to provide the basis for stable adaptive neurocontrol algorithms for a class of nonlinear plants. This paper d...

Neural network simulations of the dragonfly flight neurocontrol system have been developed to understand how this insect uses complex, unsteady aerodynamics. The simulation networks account for the ganglionic spatial distribution of cells as well as the physiologic operating range and the stochastic cellular fIring history of each neuron. In addition the motor neuron firing patterns, "flight co...

& Ersue, 1992) published a few years back on the subject of neurocontrol. The subject of neural networks for modeling and control of dynamical systems represents an important area of applications of artiÿcial neural networks that reemerged in 1980s. On the other hand, to control engineers , neurocontrol, or neural networks for controls, brings about some new and fresh ideas to the modeling and ...

The article proposes and analyzes the structure of neurocontroller for controlling vibratory drive adaptive vibrating technological machines (AVTM), which allows to implement a two-circuit neurocontrol system energy parameters AVTM that its each circuit takes into account inertial dissipative characteristics. By monitoring phase shift between frequency forced oscillations working body cyclic fo...

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 ...