با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Neural networks-based optimal tracking control for nonzero-sum games of multi-player continuous-time nonlinear systems via reinforcement learning
کنترل ردیابی بهینه مبتنی بر شبکه های عصبی برای بازی های جمع غیر صفر سیستم های غیرخطی مداوم چند نفره از طریق یادگیری تقویتی-2020 In this paper, optimal tracking control for nonzero-sum games of multi-player continuous-time nonlinear
systems is investigated by using a novel reinforcement learning scheme. Based on the multi-player nonlinear
systems and reference signal, we firstly formulate the tracking problem by constructing an augmented
multi-player nonlinear systems. The optimal tracking control problem for nonzero-sum games
of original multi-player nonlinear systems is thus transformed into solving the coupled Hamilton–
Jacobi equations of the augmented multi-player nonlinear systems. The novel neural networks (NNs) –
based online reinforcement learning (RL) method can learn the solution to coupled Hamilton–Jacobi
equations in a forward-in-time manner without requiring any value, policy iterations. In order to relax
the dependence of the traditional reinforcement learning method on Persistence of Excitation (PE) conditions,
historical data from a period of time has been collected to design NNs tuning laws. The drift
dynamic of the augmented system is not required in our scheme. The Uniformly Ultimately
Boundedness (UUB) of NNs weight errors and closed-loop augmented system states are rigorous proved.
Numerical simulation examples are given to demonstrate the effectiveness of our proposed scheme. Keywords: Neural networks | Multi-player nonzero-sum game | Optimal tracking control | Continuous-time nonlinear systems | Coupled Hamilton–Jacobi equations | Reinforcement learning |
مقاله انگلیسی |