A novel approach to approximately solving the restricted-control LQR problem online is substantiated and applied in two case-studies. The first example is a one-dimensional system whose exact solution is known. The other one refers to the temperature control of a metallic strip at the exit of a multi-stand rolling mill. The new (online-feedback) strategy employs a convenient version of the gradient method, where partial derivatives of the cost are taken with respect to the final penalization matrix coefficients and to the switching times where the control (de)saturates. The calculations are based on exact algebraic formula, which do not involve trajectory simulations, and so reducing in principle the computational effort associated with...
This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to d...
Thesis (Ph.D.)--University of Washington, 2023This dissertation makes contributions to decision-maki...
We study the problem of learning decentralized linear quadratic regulator when the system model is u...
A novel approach has been developed for approximating the solution to the constrained LQR problem, b...
Optimal and suboptimal strategies are substantiated and illustrated for linear-quadratic problems wi...
A novel approach has been developed for approximately solving the constrained LQR problem, based on ...
We consider the problem of online adaptive control of the linear quadratic regulator, where the true...
Time-optimal solutions provide us with the fastest means to regulate a system in presence of input c...
In the last century, the problem of controlling a dynamical system has been a core component in nume...
An interactive software package which provides design solutions for both standard linear quadratic r...
Optimal control theory and machine learning techniques are combined to propose and solve in closed f...
New equations involving the unknown final states and initial costates corresponding to families of L...
Optimal control theory and machine learning techniques are combined to formulate and solve in closed...
The Linear Quadratic Regulator (LQR) framework considers the problem of regulating a linear dynamica...
This paper investigates the control barrier function (CBF) based safety-critical control for continu...
This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to d...
Thesis (Ph.D.)--University of Washington, 2023This dissertation makes contributions to decision-maki...
We study the problem of learning decentralized linear quadratic regulator when the system model is u...
A novel approach has been developed for approximating the solution to the constrained LQR problem, b...
Optimal and suboptimal strategies are substantiated and illustrated for linear-quadratic problems wi...
A novel approach has been developed for approximately solving the constrained LQR problem, based on ...
We consider the problem of online adaptive control of the linear quadratic regulator, where the true...
Time-optimal solutions provide us with the fastest means to regulate a system in presence of input c...
In the last century, the problem of controlling a dynamical system has been a core component in nume...
An interactive software package which provides design solutions for both standard linear quadratic r...
Optimal control theory and machine learning techniques are combined to propose and solve in closed f...
New equations involving the unknown final states and initial costates corresponding to families of L...
Optimal control theory and machine learning techniques are combined to formulate and solve in closed...
The Linear Quadratic Regulator (LQR) framework considers the problem of regulating a linear dynamica...
This paper investigates the control barrier function (CBF) based safety-critical control for continu...
This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to d...
Thesis (Ph.D.)--University of Washington, 2023This dissertation makes contributions to decision-maki...
We study the problem of learning decentralized linear quadratic regulator when the system model is u...