In this paper, several scenarios needed for LED lighting were constructed, and the AC power input LED lighting controller was designed and implemented to operate the scenario to confirm its behavior. The hardware of the control system consists of the power unit, AVR control unit, CLCD output unit, LED control unit, scenario selection switch unit and operation speed display unit, and it is manufactured in 4-channel and 2-channel. The CPU used ATmega128 and FET FQD19N10 to control the current signal. In order to operate the CPU, DC 12V was converted to DC 5V using regulator 7805, and a heat shield was used to remove heat generated by FET. In addition, the SMPS capacity consists of a total of 200W for 4-channel controllers, and a total of 100W for 2-channel controllers. The load capacity of the LED module for each channel is 50W. In addition, LED lighting controllers and SMPS are combined to form a single control board to combine the functions of LED lighting control and SMPS to produce an LED lighting controller that can be operated with AC 220V. LED lighting control systems of the manufactured 4-channel and 2-channel were configured to check their behavior in various scenarios. Several operational scenarios identified are attached to the appendix. Verification of an action scenario can be expressed as On/Off for each scenario selection switch, the operating speed of the action scenario is adjusted by a separate speed control switch, and the speed display is expressed from the slowest zero to the fastest 9. The characteristics of the LED controller in this study are 4-channel, so four RGB LED colors can be controlled at the same time. By exchanging the terminals of the RGB LED module, it can produce more emotional lighting, select 10 color control production scenarios including mode and speed, compatibility with all existing RGB LED modules and bars, and use many RGB LEDs through large current capacity design at each channel terminal. In addition, the computer simulation was conducted by designing the control system to show the most appropriate color according to the input value of distance and illumination. As a result, given the output color and the result value according to the fuzzy rules, unlike the conventional Crisp logic, the fuzzy logic does not require the storage of many data inputs due to the nature of artificial intelligence, but is simple and has the efficiency to represent many output values at a small input value. These properties have shown that LED lighting controls through the fuzzy computation system are more organic and efficient than more common LED lighting controls.