دانلود و نمایش مقالات مرتبط با کنترل کننده بهینه::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

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). 

نتیجه جستجو - کنترل کننده بهینه

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing
یک روش یادگیری تقویت عمیق برای مدیریت عدم قطعیت نیروگاه های بادی از طریق کنترل سیستم ذخیره انرژی و خرید ذخیره خارجی-2020
In deregulated environment, the wind power producers (WPPs) will face the challenge of how to increase their revenues under uncertainties of wind generation and electricity price. This paper proposes a method based on deep reinforcement learning (DRL) to address this issue. A data-driven controller that directly maps the input observations, i.e., the forecasted wind generation and electricity price, to the control actions of the wind farm, i.e., the charge/discharge schedule of the relevant energy storage system (ESS) and the reserve purchase schedule, is trained according to the method. By the well-trained controller, the influence of the uncertainties of wind power and electricity price on the revenue can be automatically involved and an expected optimal decision can be obtained. Furthermore, a targeted DRL algorithm, i.e., the Rainbow algorithm, is implemented to improve the effectiveness of the controller. Especially, the algorithm can overcome the limitation of the conventional reinforcement learning algorithms that the input states must be discrete, and thus the validity of the control strategy can be significantly improved. Simulation results illustrate that the proposed method can effectively cope with the uncertainties and bring high revenues to the WPPs.
Keywords: Deep reinforcement learning | Energy storage system | Optimal controller | Rainbow | Reserve | Wind power producer
مقاله انگلیسی
2 Integrated Adaptive Dynamic Programming for Data-driven Optimal Controller Design
برنامه نویسی پویای تطبیقی مجتمع برای طراحی کنترل کننده بهینه داده محور-2020
In this paper a novel integrated adaptive dynamic programming method with an advantage function is developed to solve model-free optimal control problems and improve the control performance. The advantage function is utilized to evaluate the cost resulting from the action (control variables) which does not follow the optimal control policy. The Q function in Q-learning can thus be built from a value function and the advantage function. The control policy is then improved through minimizing the Q function. To employ the proposed algorithm, an integrated multi-layer neural network (INN) is designed for the value function and the control variables. Only one single neural network requires adaption. This avoids the iterative learning of two separate networks in the heuristic dynamic programming-based methods. Simulation for linear and non-linear optimal control problems is studied. Comparing to the optimal solutions resulting from the linear quadratic regulator and dynamic programming (DP), the proposed INN design can lead to closer control performance than the ones with action dependent heuristic dynamic programming (ADHDP). Furthermore INN is applied to optimize the energy management strategy of hybrid electric vehicles for fuel economy. The fuel consumption based on INN is lower than the one from ADHDP and much closer to the optimal results by DP. The result indicates the near fuel-optimality and an effective practical application.
Keywords: Adaptive dynamic programming | reinforcement learning | integrated neural network | advantage function | learning control | energy management strategy
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 8968 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 8968 :::::::: افراد آنلاین: 75