دانلود ترجمه مقاله شبکه عصبی تناسبی انتگرالی مشتقی PID در سیستم های تاخیر زمانی - مجله الزویر

۳ ۱ لایه ی ورودی
۳ ۲ لایه ی پنهان
۳ ۳ لایه خروجی
۳ ۴ الگوریتم انتشار روبه عقب

۴ مثال ها

۴ ۱ سیستم یک گامی
۴ ۲ سیستم دو گامی
۴ ۳ عملکرد کنترلر تناسبی-انتگرالی-مشتقی مرسوم و متدوال

۵ نتایج

There are a lot of time-delay systems in industry processes but it is difficult to design the controllers for them because the time-delay property. These systems generally have larger overhead, longer adjusting time and are not stable. In classical control theory the Smith method can be used to construct controllers if the transfer function of the system has been known. But, the transfer function of a practical system is not easy to measure or to complete. As is well known conventional PID controllers have many advantages so that they are most widely used in various fields of the industry, especially in the processes of chemical industry. Although PID controllers have strong abilities they are not suitable for the control of long time-delay systems, in which the P, I, and D parameters are difficult to chose. Artificial neural networks can perform adaptive control through learning processes. But there are some problems, which should be solved in practice. The main problems are the slow learning speed, the long weight convergence time and uncertain property. PID neural network (PIDNN) is a new kind of networks. It utilizes the advantages of both PID control and neural structure. It consists of proportional (P), integral (I) and derivative (D) neurons and its weights are adjusted by the back-propagation algorithms. It can control different systems through quick learning process and has perfect performances (Shu, 1997, 1998a,b,c; Shu & Li, 1998; Shu, 1999a,b. The rest of the paper is organized as follows. Section 2 presents the structure of PIDNN. Section 3 specifies the algorithm of PIDNN. System simulation examples are introduced in Section 4, including the performance behavior comparing between PIDNN and conventional PID controllers. Finally, the conclusion is given in Section 5.