Online approximation of an optimal station keeping strategy for a fully actuated six degrees-of-freedom autonomous underwater vehicle is considered. The developed controller is an approximation of the solution to a two player zero-sum game where the controller is the minimizing player and an external disturbance is the maximizing player. The solution is approximated using a reinforcement learni...

Online approximation of an infinite horizon optimal path-following strategy for a kinematic unicycle is considered. The solution to the optimal control problem is approximated using an approximate dynamic programming technique that uses concurrent-learning-based adaptive update laws to estimate the unknown value function. The developed controller overcomes challenges with the approximation of t...

In deterministic systems, reinforcement learningbased online approximate optimal control methods typically require a restrictive persistence of excitation (PE) condition for convergence. This paper presents a concurrent learningbased solution to the online approximate optimal regulation problem that eliminates the need for PE. The development is based on the observation that given a model of th...

Approximate/adaptive dynamic programming (ADP) has been studied extensively in recent years for its potential scalability to solve large state and control space problems, including those involving continuous states and continuous controls. The applicability of ADP algorithms, especially the adaptive critic designs has been demonstrated in several case studies. Direct heuristic dynamic programmi...

هدف انتخاب تأمین‌کنندگان و تخصیص سفارش بر اساس ابعاد پایداری ریسک پایداری، در شرایط عدم‌­قطعیت برخی پارامترها است؛ همچنین با محدودیت استراتژی کاهش برای مدیریت همراه است. معیارهای ریسک، امتیاز توسط روش‌های تاپسیس فازی تجزیه‌وتحلیل آثار شکست محاسبه شده است سپس برنامه تصادفی چندمرحله‌ای ایجاد معیار ارزش معرض شرطی، پیرامون تأمین منابع یک فضای چند­دوره‌ای برنامه‌‎ریزی دستیابی به برنامه‌ریزی انعطاف‌پ...

In recent times, a variety of reinforcement learning (RL) algorithms have been proposed for optimal tracking problem continuous time nonlinear systems with input constraints. Most these are based on the notion uniform ultimate boundedness (UUB) stability, in which normally higher rates avoided order to restrict oscillations state error smaller values. However, this comes at cost convergence cri...

The surface detectors of the Pierre Auger Observatory are being upgraded by adding new and replacing electronics. upgrade project, called “AugerPrime,” includes addition a small PMT to increase dynamic range particle counting, plastic scintillator above each water-Cherenkov detector improve discrimination between electromagnetic muonic shower components, radio measure emission from inclined air...

In this paper, we design a nonlinear controller for the kinematic model of an underactuated rigid spacecraft that ensures uniform, ultimately bounded (UUB) tracking provided the initial errors are selected sufficiently small. The result is achieved via a judicious formulation of the spacecraft kinematics and the novel design of a Lyapunov-based controller. We also discuss how standard backstepp...

In this paper, we utilize a Lyapunov-based design approach to construct a visual servoing controller for a robot manipulator that ensures uniformly ultimately bounded (UUB) endeffector position tracking performance despite parametric uncertainty throughout the entire robot/camera system. The UUB end-effector tracking result exploits information from both a fixed camera and a camera-in-hand, alt...