Underwater robots require adequate guidance and control to perform useful tasks. Visual information is important to these tasks and visual servo control is one method by which guidance can be obtained. To coordinate and control thrusters, complex models and control schemes can be replaced by a connectionist learning approach. Reinforcement learning uses a reward signal and much interaction with...

Abstract—This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMAL2...

One of the major challenges in the field of neurally driven evolved autonomous agents is deciphering the neural mechanisms underlying their behavior. Aiming at this goal, we have developed the multi-perturbation Shapley value analysis (MSA)--the first axiomatic and rigorous method for deducing causal function localization from multiple-perturbation data, substantially improving on earlier appro...

Vision has extensively expanded the robots capabilities, making the robot control problem more complex. To track a target with a robot arm in a three-dimensional space involves to use precise commands. We propose to insert a hierarchical neurocontroller based on CMAC (Cerebellar Model Articulation Controller) networks in a visual servoing loop. This hierarchical structure splits the robot’s wor...

A model-based reinforcement learning algorithm is developed in this paper for fixed-final-time optimal control of nonlinear systems with soft and hard terminal constraints. Convergence of the algorithm, for linear in the weights neural networks, is proved through a novel idea by showing that the training algorithm is a contraction mapping. Once trained, the developed neurocontroller is capable ...

This study focuses on the synthesis of a robust neurocontroller for a direct drive robotic manipulator having two degrees of freedom. The method presented in the paper uses the Variable Structure Systems methodology in stabilizing the gradient descent based training dynamics of the controller. The use of this approach introduces the robustness of variable structure systems based design framewor...