Adaptation 凵 mechanlsm r(t) u(t controller plant FIGURE 4.1 direct adaptive controls 6
6 FIGURE 4.1 direct adaptive controls. Adaptation mechanism controller plant r(t) u(t) y(t)
In Section 4.2 we use a simple example to introduce a method for direct (model reference) adaptive fuzzy control where the controller that is tuned is a fuzzy controller. Next, we provide several design and implementation case studies to show how it compares to conventional adaptive control for a ship steering application, how to make it work for a multi-input multi-output MIMO) fault-tolerant aircraft control problem, and how it can perform in implementation for the two-link flexible robot
7 In Section 4.2 we use a simple example to introduce a method for direct (model reference) adaptive fuzzy control where the controller that is tuned is a fuzzy controller. Next, we provide several design and implementation case studies to show how it compares to conventional adaptive control for a ship steering application, how to make it work for a multi-input multi-output (MIMO) fault-tolerant aircraft control problem, and how it can perform in implementation for the two-link flexible robot
In the second general approach to adaptive control, which is shown in Figure 4.2, 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
8 In the second general approach to adaptive control, which is shown in Figure 4.2, 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
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 designer can specify a controller for each set of plant parameter estimates, it will succeed in controlling the plant. The overall approach is called"indirect adaptive control since we tune the controller indirectly by first estimating the plant parameters(as opposed to direct adaptive control, where the controller parameters are estimated directly without first identifying the plant parameters
9 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 designer can specify a controller for each set of plant parameter estimates, it will succeed in controlling the plant. The overall approach is called "indirect adaptive control" since we tune the controller indirectly by first estimating the plant parameters (as opposed to direct adaptive control, where the controller parameters are estimated directly without first identifying the plant parameters)
Plant Controller parameters System designer identification Controller parameters r(t y controller plant FIGURE 4.2 indirect adaptive controls
10 FIGURE 4.2 indirect adaptive controls. System identification controller plant r(t) u(t) y(t) Controller designer Plant parameters Controller parameters