A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm (ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters Kp* , Ti*. and Td* can be obtained by taking the overshoot, settling time, and steady-state error of the system’s unit step response as the performance indexes and by use of our improved ant system algorithm. Kp* , Ti* , and Td* can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters,including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA).were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better per A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm (ASA). For a given control system with this kind of PID controller, a group of optimal PID controller parameters Kp *, Ti *. And Td * can be obtained by taking the overshoot, settling time, and steady-state error of the system’s unit step response as the performance indexes and by use of our improved ant system algorithm. Kp *, Ti *, and Td * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters , including the Ziegler-Nichols method (ZN), the genetic algorithm (GA), and the simulated annealing (SA) .were taken for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better per