||Proportional-integral-differential (PID) controllers are the most commonly used controllers in the process industrial due to their simplicity in structure and robustness in performance. However, PID controllers are linear controllers so that if the processes under control are nonlinear, the performance would become poor. To deal with nonlinear processes, we need nonlinear PID controllers. Fuzzy PID controllers are a kind of nonlinear PID controllers that use fuzzy logic principles to specify the structures of the controllers. In this thesis, we propose two methods to design fuzzy PID controllers and apply the approaches to the control of the hot strip mill in the iron-and-steel industry. In the first approach, the parameters of the fuzzy PID controller are determined through adaptive control principle. Specifically, we first fix the structure of the fuzzy PID controller and let its parameters free to change. Then, based on the Lyapunov synthasis idea, we design an adaptation law to tune the free parameters of the fuzzy PID controller on-line. Finally, the parameters of the fuzzy PID controller are obtained as the converged values given by the adaptation law. In the second approach, the parameters of the fuzzy PID controller are determined using a table look-up scheme. Based on common sense and the heuristics in the design of conventional PID controllers, a look-up table is constructed which shows how to compute the fuzzy PID parameters in different situations. These rules are used to specify the parameters on-line. The two methods above are simulated for a number of typical plants and the the results show that the performance using the fuzzy PID controllers is generally better than that using the traditional linear PID controllers. Finally, we test the two methods on a hot strip mill model which is nonlinear and the results show that the fuzzy PID controllers give good performance.