A classic Harvard study shows the impact of overestimating your ability to control outcomes, especially when you experience early success.

Sollum Technologies has announced a strategic partnership with AI greenhouse control company Optimal. This collaboration integrates Sollum’s 100% dynamic LED lighting with Optimal's AI climate and ...

This brief studies the optimal control policy learning problem for discrete-time linear systems. A data-driven model-free algorithm is proposed by using the data matrices of the augmented system state ...

Liquefied Petroleum Gas (LPG) is a crucial energy source, widely utilized in residential, industrial, agricultural, and transportation sectors, where its safe and efficient use relies on accurate ...

The problems were solved using DIDO©, a MATLAB® toolbox for solving optimal control problems. 1 DIDO implements a guess-free, 2 fast spectral algorithm based on pseudospectral optimal control theory.

In this article, we study the optimal control problem for steering the state covariance of a discrete-time linear stochastic system over a finite time horizon. First, we establish the existence and ...

However, not all self-control behaviors are useful in leadership. It’s important to distinguish between productive versus unproductive aspects of self-control.

Learn the basics and benefits of adaptive and optimal control, and how to decide which one is more suitable for your electrical engineering system and problem.

This repo contains the code for the paper Stochastic Optimal Control Matching. We propose the SOCM training loss to learn controls in stochastic optimal control problems. We compare it to the ...

Learn about the latest developments and trends in optimal control theory and practice, a branch of control systems design that finds the best control inputs.

Optimal control theory is a mathematical framework used to determine the best possible control strategy for a dynamic system to achieve a desired objective while satisfying constraints.