multi-input multi-output(MIMO) fault-tolerant aircraft control problem. Following this, in Section 3. 4 we show several ways to "dynamically focus"the learning activities of an adaptive fuzzy controller. A simple magnetic levitation control problem is used to introduce the methods, and we compare the performance of the methods to a conventional adaptive control technique Design and implementation case studies are ovided for the rotational inverted pendulum(with a sloshing liquid in a bottle at the endpoint) Adaptation mechani sm r(t) u( y(t) controller plant In the second general approach to adaptive control, which is shown in Figure 3. 2, we use an on-line syster identification method to estimate the parameters of the plant and a"controller designer"module to subsequently specify the parameters of the controller Controll paramete. System designer identification Controller parameter r(t) u(t controller plant Figure 3. 2 indirect adaptive controls If the plant parameters change, the identifier will provide estimates of these and the controller designer will subsequently tune the controller. It is inherently assumed that we are certain that the estimated plant parameters are equivalent to the actual ones at all times(this is called the "certainty equivalence principle"). Then if the controller PDF文件使用" pdffactory Pro"试用版本创建ww. fineprint,com,cnmulti-input multi-output (MIMO) fault-tolerant aircraft control problem. Following this, in Section 3.4 we show several ways to "dynamically focus" the learning activities of an adaptive fuzzy controller. A simple magnetic levitation control problem is used to introduce the methods, and we compare the performance of the methods to a conventional adaptive control technique. Design and implementation case studies are provided for the rotational inverted pendulum (with a sloshing liquid in a bottle at the endpoint). Figure 3.1 direct adaptive controls. In the second general approach to adaptive control, which is shown in Figure 3.2, we use an on-line system identification method to estimate the parameters of the plant and a "controller designer" module to subsequently specify the parameters of the controller. Figure 3.2 indirect adaptive controls. If the plant parameters change, the identifier will provide estimates of these and the controller designer will subsequently tune the controller. It is inherently assumed that we are certain that the estimated plant parameters are equivalent to the actual ones at all times (this is called the "certainty equivalence principle"). Then if the controller PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn