The paper proposes a method for structured state-feedback controllers design linear time-invariant systems. A necessary and sufficient condition stabilizability of systems, making an appeal to the linear-quadratic (LQ) regulator theory, is first proposed. latter presented in form nonlinear matrix equation. Then, it recast as nonsmooth unconstrained equation using projection onto positive semi-d...

This report presents a method of controlling an unmanned ground vehicel (UGV) by employing hybrid control theory. The linear dynamics of the UGV near a straight path conform to modern linear quadratic regulator theory. Two different methods for regulation of the UGV on a straight track are analyzed. The first method employs the steering angle as an input, while the second employs the derivative...

In this paper, the finite-horizon near optimal adaptive regulation of linear discrete-time systems with unknown system dynamics is presented in a forward-in-time manner by using adaptive dynamic programming and Q-learning. An adaptive estimator (AE) is introduced to relax the requirement of system dynamics, and it is tuned by using Q-learning. The time-varying solution to the Bellman equation i...

We revisit in this paper the discrete-time linear quadratic regulator (LQR) problem from perspective of receding-horizon policy gradient (RHPG), a newly developed model-free learning framework for control applications. provide fine-grained sample complexity analysis RHPG to learn that is both stabilizing and -close optimal LQR solution, our algorithm does not require knowing initialization. Com...

A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) attitude (roll, pitch, yaw) control of a quadrotor vehicle. The derivation mathematical model the formulated based on Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain superior performance. Therefore, we exploit advantages nature-inspired...