Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective...

In optimal control theory, a control is a variable chosen by the controller or agent to manipulate state variables, similar to an actual control valve...

for this reason MPC is also called receding horizon control. Although this approach is not optimal, in practice it has given very good results. Much academic...

the optimal policy in the last time period is specified in advance as a function of the state variable's value at that time, and the resulting optimal value...

In optimal control problems, it is sometimes the case that a control is restricted to be between a lower and an upper bound. If the optimal control switches...

a cost function where a minimum implies a set of possibly optimal parameters with an optimal (lowest) error. Typically, A is some subset of the Euclidean...

unscented optimal control combines the notion of the unscented transform with deterministic optimal control to address a class of uncertain optimal control problems...

problem – to movement trajectories. Active inference is related to optimal control by replacing value or cost-to-go functions with prior beliefs about...

sufficient conditions for optimality of a control with respect to a loss function. Its solution is the value function of the optimal control problem which, once...

Pseudospectral optimal control is a numerical technique for solving optimal control problems. These problems involve finding the best way to control a dynamic...

and the control strategy chosen. List of the main control techniques Optimal control is a particular control technique in which the control signal optimizes...

The Hamiltonian is a function used to solve a problem of optimal control for a dynamical system. It can be understood as an instantaneous increment of...

the natural outcome of an adaptive optimal control process. Optimal control is a way of understanding motor control and the motor equivalence problem,...

Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations...

same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends...

Bellman's principle of optimality, a related approach to optimal control problems which states that the optimal trajectory remains optimal at intermediate points...

learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic...

The PROPT MATLAB Optimal Control Software is a new generation platform for solving applied optimal control (with ODE or DAE formulation) and parameters...

included developments in optimal control in the 1950s and 1960s followed by progress in stochastic, robust, adaptive, nonlinear control methods in the 1970s...

the three control terms of proportional, integral and derivative influence on the controller output to apply accurate and optimal control. The block...

object in question. The search for optimal values for object or system characteristics is carried out by means of optimal change to design, geometrical or...

engineering (GRAPE) algorithm for quantum optimal control. The documentation and the book describing the optimal control module of the package list the following...

The theory of optimal control is concerned with operating a dynamic system at minimum cost. The case where the system dynamics are described by a set...

the optimal profit to a player is calculated subject to a constrained space of actions, where a Lagrange multiplier is the change in the optimal value...

optimization is a technique for computing an open-loop solution to an optimal control problem. It is often used for systems where computing the full closed-loop...

backpropagation appeared in optimal control theory since 1950s. Yann LeCun et al credits 1950s work by Pontryagin and others in optimal control theory, especially...

"Estimation and nonlinear optimal control: Particle resolution in filtering and estimation". Studies on: Filtering, optimal control, and maximum likelihood...

Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with...

optimization and optimal control problems. GEKKO is used for advanced control in the Temperature Control Lab (TCLab) for process control education at 20...

In control theory, the linear–quadratic–Gaussian (LQG) control problem is one of the most fundamental optimal control problems, and it can also be operated...