A fundamental theory of deterministic linear-quadratic (LQ) control is the equivalent relationship between problems, two-point boundary value and Riccati equations. In this paper, we extend equivalence to a general time-inconsistent LQ problem, where inconsistency arises from nonexponential discount functions. By studying solvability equation, show existence uniqueness linear equilibrium for pr...

Recent research on reinforcement learning has focused on algorithms based on the principles of Dynamic Programming (DP). One of the most promising areas of application for these algorithms is the control of dynamical systems, and some impressive results have been achieved. However, there are significant gaps between practice and theory. In particular, there are no con vergence proofs for proble...

In this study Response Surface Methodology was used to optimize process conditions and to evaluate the effect of ultrasound on quality attributes (antioxidant activity, pH, total soluble solid, turbidity) and the inactivation of Escherichia coli bacteria in carrot juice. Independent variables in this study were temperature (25-50°C), time (20-40 min) and frequency (0-130 kHz). In this study the...

In this study Response Surface Methodology was used to optimize process conditions and to evaluate the effect of ultrasound on quality attributes (antioxidant activity, pH, total soluble solid, turbidity) and the inactivation of Escherichia coli bacteria in carrot juice. Independent variables in this study were temperature (25-50°C), time (20-40 min) and frequency (0-130 kHz). In this study the...

This paper studies the robustness of reinforcement learning algorithms to errors in process. Specifically, we revisit benchmark problem discrete-time linear quadratic regulation (LQR) and study long-standing open question: Under what conditions is policy iteration method robustly stable from a dynamical systems perspective? Using advanced stability results control theory, it shown that for LQR ...