In this paper, we will deal with a Linear Quadratic Optimal Control problem unknown dynamics. As modeling assumption, suppose that the knowledge an agent has on current system is represented by probability distribution $\pi$ space of matrices. Furthermore, assume such measure opportunely updated to take into account increased experience obtains while exploring environment, approximating increas...

This technical article investigates the linear quadratic regulator (LQR) design for continuous-time positive systems. Based on systems theory and Lyapunov theory, solvability optimality of positivity-preserving LQR problem are analyzed through lens optimization, two projection theorems derived single-input multi-input systems, respectively, which paves way developing a projected gradient descen...

According to the existed structure and algorithm of extension controller, proposed an improved extension control algorithm based on the Optimal Control, which was named LQR-EC, and applied to a SIMO system—Crane-Double Pendulum System. And, using MATLAB simulation platform to study the effect of the LQR—EC Algorithm. The result shows that, the LQR-EC Algorithm not only has a simple theory, but ...

Abstract – The modeling and control of 3 Degrees-of-Freedom (DOF) four-rotor rotorcraft is presented in this paper. Optimal control (LQR), LQR with gain scheduling, feedback linearization and sliding-mode control are simulated and tested on an experimental rig. The performance of the individual controllers are compared and discussed. Our simulation showed Sliding Mode Control (SMC) returned the...

An autonomous distributed LQR/APF control algorithm for multiple small spacecraft during simultaneous close proximity operations has been developed. This research contributes to the control of multiple small spacecraft for emerging operation, which may include inspection, assembly, or servicing. A control algorithm is proposed which combines the control effort efficiency of the Linear Quadratic...

A standard assumption in traditional (deterministic and stochastic) optimal (minimizing) linear quadratic regulator (LQR) theory is that the control weighting matrix in the cost functional is strictly positive definite. In the deterministic case, this assumption is in fact necessary for the problem to be wellposed because positive definiteness is required to make it a convex optimization proble...

The H2-optimal controller for systems with preview, in which the knowledge of external input is available in advance for the controller, is derived. The single input case is first considered and solved by transforming the problem into a non-standard LQR problem. It turns out that the extensions to multiple inputs and multiple preview times cases are straightforward. In every case considered, th...

Linear quadratic regulator (LQR) with trajectory sensitivity minimization and structurally constrained controller was formulated in [2] and [4]. In these works, a gradient search method, which is a particular type of nonlinear programming, was used to solve the problem. In this paper, we propose a linear matrix inequality (LMI)-based method for the problem. This LMI is formulated with block-dia...

Non-Linear Dimensionality Reduction (NLDR) techniques such as ISOMAP, LLE, Laplacian Eigenmaps etc. attempt to estimate low-dimensional latent descriptors for data assumed to be drawn from an m-dimensional manifold in an ambient n-dimensional space. Out-ofSample Extension the problem of estimating the latent vectors for novel data has attracted considerable attention in the literature. In this ...

Proposed are generalizations and refinements of a well-known result on robust matrix sign-definiteness, which is extensively exploited in quadratic stability, design of robust quadratically stabilizing controllers, robust LQR-problem, etc. The main emphasis is put on formulating the results in terms of linear matrix inequalities.