AIMC Topic: Feedback

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Bioinspired pursuit with a swimming robot using feedback control of an internal rotor.

Bioinspiration & biomimetics
Theoretical guarantees of capture become complicated in the case of a swimming fish or fish robot because of the oscillatory nature of the fish heading. Building on the connection between a swimming fish and the canonical Chaplygin sleigh, a novel st...

A biarticular passive exosuit to support balance control can reduce metabolic cost of walking.

Bioinspiration & biomimetics
Nowadays, the focus on the development of assistive devices just for people with mobility disorders has shifted towards enhancing physical abilities of able-bodied humans. As a result, the interest in the design of cheap and soft wearable exoskeleton...

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches.

Sensors (Basel, Switzerland)
Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated "smart" devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniqu...

CFD-based multi-objective controller optimization for soft robotic fish with muscle-like actuation.

Bioinspiration & biomimetics
Soft robots take advantage of rich nonlinear dynamics and large degrees of freedom to perform actions often by novel means beyond the capability of conventional rigid robots. Nevertheless, there are considerable challenges in analysis, design, and op...

Finite-time synchronization of fractional-order gene regulatory networks with time delay.

Neural networks : the official journal of the International Neural Network Society
As multi-gene networks transmit signals and products by synchronous cooperation, investigating the synchronization of gene regulatory networks may help us to explore the biological rhythm and internal mechanisms at molecular and cellular levels. We a...

Exponential and adaptive synchronization of inertial complex-valued neural networks: A non-reduced order and non-separation approach.

Neural networks : the official journal of the International Neural Network Society
This paper mainly deals with the problem of exponential and adaptive synchronization for a type of inertial complex-valued neural networks via directly constructing Lyapunov functionals without utilizing standard reduced-order transformation for iner...

Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a novel differential-game based neural network (NN) control architecture to solve an optimal control problem for a class of large-scale nonlinear systems involving N-players. We focus on optimizing the usage of the computati...

Haptic Teleoperation of UAVs Through Control Barrier Functions.

IEEE transactions on haptics
This article presents a novel approach to haptic teleoperation. Specifically, we use control barrier functions (CBFs) to generate force feedback to help human operators safely fly quadrotor UAVs. CBFs take a control signal as input and output a contr...

A new fixed-time stability theorem and its application to the fixed-time synchronization of neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, we derive a new fixed-time stability theorem based on definite integral, variable substitution and some inequality techniques. The fixed-time stability criterion and the upper bound estimate formula for the settling time are different ...

Evolving artificial neural networks with feedback.

Neural networks : the official journal of the International Neural Network Society
Neural networks in the brain are dominated by sometimes more than 60% feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use tran...