An optimal FID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters Kp* , Ti* , and Td* are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , 77 , and Td* and according to the current system error e and its time derivative, a dedicated program is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mechanism to ensure that the system response has optimal dynamic and steady-state performance. The controller has been used to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. Th An optimal FID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In the off- line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters Kp *, Ti *, and Td * are obtained, which are used as the initial values ​​for the on-line tuning of PID parameters. In the on-line part, based on Kp *, 77, and Td * and according to the current system error e and its time derivative, a dedicated program is written , which is used to optimize and adjust the PID parameters on line through a fuzzy inference mechanism to ensure that the system response has optimal dynamic and steady-state performance. The controller has been used to control the DC motor of the intelligent bionic artificial leg designedby the authors. Th