When research in evolutionary robotics (ER) initially took off in the early 1990s, concerns over the brittleness of traditional AI techniques had recently led to a resurgence of interest in artificial neural networks (ANNs). This fact, coupled with the obvious (loose) analogy between robot control systems and biological nervous systems, meant that most ER researchers naturally gravitated toward...

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and pa...

Artificial Neural Network (ANN) is an attractive solution for the identification and control of complex systems. This technique can be very heavy for processes depending on many parameters like, for example, in steel-making. In such cases, solutions such as Orthogonal Weight Estimator (OWE) architectures are recommanded. They make it possible the definition of a generic neuronal architecture, a...

Innovative solar system exploration missions require ever larger velocity increments and thus ever more demanding propulsion capabilities. Using for those high-energy missions the stateof-the-art technique of chemical propulsion in combination with (eventually multiple) gravity assist maneuvers results in long, complicated, and inflexible mission profiles. Low-thrust propulsions systems can sig...

A common technique in neurocontrol is that of controlling a plant by static state feedback using the plant's inverse dynamics, which is approximated through a learning process. It is well known that in this control mode even small approximation errors or, which is the same, small perturbations of the plant may lead to instability. Here, a novel approach is proposed to overcome the problem of in...

Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constr...

A common t.echnique in neurocontrol is that. of controlling a plant by static state feedback using the plant's inverse dynamics , which is approximated t.hrough a learning proce:flfl. It is ,"vdl knu\vn that in t.his control mode: e:ve:n small approximation errors or, which is the same, small perturbations of the plant may lead to insta­ bility. Here, a novel approach is proposed to overcome th...

In adaptive dynamic programming, neurocontrol and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimise a total cost function. In this paper we show that when discretized time is used to model the motion of the agent, it can be very important to do “clipping” on the motion of the agent in the final time step of the trajectory. By clipping we mean tha...

A system is described which uses system realization and `gradient descent through frozen model networks' for learning the sequential generation of fovea trajectories such that the nal position of a moving fovea corresponds to a target in a visual scene. The target may be arbitrarily rotated and translated, and it might even move. No teacher provides the desired activations of `eye-muscles' at v...

The concept of approximate dynamic programming and adaptive critic neural network based optimal controller is extended in this study to include systems governed by partial differential equations. An optimal controller is synthesized for a dispersion type tubular chemical reactor, which is governed by two coupled nonlinear partial differential equations. It consists of three steps: First, empiri...