||A vaccine-immune algorithm based fuzzy neural network (VCIAFNN) controller is proposed in this thesis. The traditional GA has better solutions on global searching, but lack of the ability of local searching and the premature in action of the crossover and mutation. To overcome the blindness, the VCIA estimator will use the clonal selection principle and vaccines inoculation, respectively. The clonal selection principle cancels antibodies with high similar degree, and vaccines inoculation can lead the parameters to unexplored areas. After two steps of the above, the parameters will have higher quality, accelerating the convergence, and increasing the global search capacity. The initial values of the centers, left-widths, and right-widths of the Gaussian functions, link weights, and the learning rates of the adaptive laws are obtained by VCIA estimator. Next, the parameters of FNN estimator are updated by adaptive laws in the online phase. Finally, the hitting control is utilized to eliminated the uncertainties and external disturbances of the nonlinear system combine with the output of computation controller to form the main control effort. The presented VCIAFNN is adopted to solve several nonlinear control problems. The simulation results have shown that the proposed VCIAFNN can outperform other methods.