Model predictive control—status and challenges
X Yu-Geng, L De-Wei, L Shu - Acta Automatica Sinica, 2013 - Elsevier
For the last 30 years the theory and technology of model predictive control (MPC) have been
developed rapidly. However, facing the increasing requirements on the constrained …
developed rapidly. However, facing the increasing requirements on the constrained …
Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization
Portfolio optimization is one of the most important investment strategies in financial markets.
It is practically desirable for investors, especially high-frequency traders, to consider …
It is practically desirable for investors, especially high-frequency traders, to consider …
Application of neural network models for mathematical programming problems: a state of art review
K Lachhwani - Archives of Computational Methods in Engineering, 2020 - Springer
Artificial neural networks or neural networks (NN) are new computational models based on
the working of biological neurons of human body. A NN model consists of an interactive …
the working of biological neurons of human body. A NN model consists of an interactive …
Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective
This paper considers cooperative kinematic control of multiple manipulators using
distributed recurrent neural networks and provides a tractable way to extend existing results …
distributed recurrent neural networks and provides a tractable way to extend existing results …
Distributed task allocation of multiple robots: A control perspective
The problem of dynamic task allocation in a distributed network of redundant robot
manipulators for pathtracking with limited communications is investigated in this paper …
manipulators for pathtracking with limited communications is investigated in this paper …
Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks
In this paper, we present a neurodynamic approach to model predictive control (MPC) of
unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The …
unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The …
Dynamic task allocation in multi-robot coordination for moving target tracking: A distributed approach
A new coordination control is developed in this paper for multiple non-holonomic robots in a
competitive manner for target tracking with limited communications. In this proposed control …
competitive manner for target tracking with limited communications. In this proposed control …
Kinematic control of redundant manipulators using neural networks
Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent
neural networks (RNNs), as inherently parallel processing models for time-sequence …
neural networks (RNNs), as inherently parallel processing models for time-sequence …
A one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints
Q Liu, J Wang - IEEE Transactions on Neural Networks and …, 2013 - ieeexplore.ieee.org
This paper presents a one-layer projection neural network for solving nonsmooth
optimization problems with generalized convex objective functions and subject to linear …
optimization problems with generalized convex objective functions and subject to linear …