The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inver...

Formation control of network of multi-agent systems with heterogeneous nonlinear dynamics is formulated as an optimal tracking problem and a decentralized controller is developed using the framework of ‘adaptive critics’ to solve the optimal control problem. The reference signal is assumed available only in online implementation so its dynamics is unavailable for offline training of the neuroco...

In this paper an extension of SANE that simultaneously evolves the weights and architecture of an MLP neural network is presented. The symbiotic adaptive neuroevolution (SANE) system coevolves a population of neurons that cooperate to form a functioning neural network. Evolutionary Strategies (ES) is applied to evolve the network weights. In order to increase the evolving system performance and...

This article presents a novel method for the evolution of artificial autonomous agents with small neurocontrollers. It is based on adaptive, self-organized compact genotypic encoding (SOCE) generating the phenotypic synaptic weights of the agent's neurocontroller. SOCE implements a parallel evolutionary search for neurocontroller solutions in a dynamically varying and reduced subspace of the or...

Flow control is attracting an increasing attention of researchers from a wide spectrum of specialties because of its interdisciplinary nature and the associated challenges. One of the main goals of The Collaborative Center of Control Science at The Ohio State University is to bring together researchers from different disciplines to advance the science and technology of flow control. This paper ...

The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel con...

This article presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe functional contribution analysis (FCA), which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multilesions is maximized. The approach is thoroughly ...

The combination of low-thrust propulsion and gravity assists to enhance deep-space missions has proven to be a remarkable task. In this paper, we present a novel method that is based on evolutionary neurocontrollers. Themain advantage in the use of a neurocontroller is the generation of a control law with a limited number of decision variables. On the other hand, the evolutionary algorithm allo...

We developed a biologically plausible control algorithm to move the eyes of a six degrees of freedom robotic head in a human-like manner. Our neurocontroller, written with the neural simulator Nengo, integrates different biological neural models of eye movements, such as microsaccades, saccades, vestibular-ocular reflex, smooth pursuit and vergence. The coordination of the movements depends on ...