Optimal operation of solar plant is a challenge due to the multiple disturbances affecting the
plant. Research in optimal temperature set-point tracking is extensive whereas research …

Optimal operation of a solar plant is generally understood as a tracking of the optimal
working temperatures which maximize the net electric power. However, a commercial solar …

The problem of nonaffine time-varying nonlinear control systems is addressed in this paper
through an adaptive state-space neuro-fuzzy control scheme. It combines an eight-layered …

Multi-physical field sensing and machine learning have drawn great attention in various
fields such as sensor networks, robotics, energy devices, smart buildings, intelligent system …

针对一类强耦合并且带非线性不可测干扰的线性系统, 提出了一种基于神经网络干扰观测器的
近似最优解耦控制方法. 首先, 采用递归神经网络对干扰进行观测; 然后设计前馈解耦控制器 …

This paper proposes to use model predictive control with binary-regularization (MPC BR) to
manage the generation problem in concentrating solar power (CSP) plants with thermal …

We propose an approximate optimal decoupling control method based on a neural network
disturbance observer for a class of linear systems with strong couplings and nonlinear …

Currently, governments are promoting the use of renewable energy sources to reduce the
environmental impact produced by the use of fossil fuels [8, 28]. Solar energy is, by far, the …

Optimal operation of a solar plant is generally understood as a tracking of the optimal
working temperatures which maximize the net electric power. However, a commercial solar …