This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRAC) scheme to control arbitrary complex nonlinear systems. As the training method for the WNN, a newly developed optimization technique, called the micro artificial immune system (Micro-AIS), is employed to find the optimal values for the WNN parameters. Two modifications were suggested to enhance the performance of the original Micro-AIS, resulting in a more powerful optimization algorithm. Utilizing the proposed control approach, it is not necessary to construct a pseudo-plant, which was a prerequisite in other works, for controlling the nonlinear systems. To demonstrate the effectiveness of the proposed direct WNNMRAC, three single-input single-output complex nonlinear systems are selected, including a non-minimum phase system, a time-delay system, and a minimum phase system. From several performance evaluation tests, the WNNMRAC has shown its effectiveness in terms of accurate control performance, applicability to different types of nonlinear systems, robustness to external disturbances, and good generalization ability. In addition, a simulation test to control nonlinear multi-input multi-output (MIMO) system has shown that the WNNMRAC can be extended to control nonlinear MIMO systems. Finally, from a comparative study, the WNNMRAC has confirmed its superiority over a conventional neural network model reference adaptive control.