PID（Proportion，Integral，Differential）即比例、积分、微分控制规律的控制算法是目前控制系统中应用最多的一种算法。如果PID整定过程只是单纯的依靠手动来完成，那将是非常麻烦和耗时的。目前工业现场中因为操作人员的经验问题而导致许多控制回路整定效果不佳，因此PID自整定对于研究者和现场工程师的操作具有及其重要的意义。 为了解决上述问题，本论文较为深入地研究了PID自整定技术的理论和应用，并结合GA（Genetic algorithm，遗传算法）和BP（Back Propagation）神经网络，提出了一种基于GA优化的BP神经网络的PID自整定控制算法。由于GA具有全局搜索性，而BP神经网络对非线性逼近能力强，但易陷入局部极小。现将二者结合，从而提高了BP神经网络的权系数学习效率，减小了陷入局部解的可能性，能够快速地达到全局收敛，得到最优参数的控制器。 接着本文针对所研究的算法设计了程序并进行了仿真。所设计的程序包括：常规PID控制、GA优化的PID控制、BP神经网络PID控制和基于GA优化的BP神经网络PID控制。经过对仿真结果分析与比较表明：基于GA优化的BP神经网络的PID自整定控制算法不仅能够提高算法在训练过程中的收敛速度，而且训练后的BP神经网络具有较强的自适应和自学习能力，从而进一步提高了控制器的性能。 最后针对本文提出的算法设计了一个水箱恒温控制系统，并用VC++开发工具，开发了上位机软件。然后用该系统对所研究的算法进行实验验证，对结果进行了对比分析。实验表明了遗传BP神经网络PID参数整定算法在实际应用中的可行性。

PID (Proportion, Integral, Differential) algorithm, one of the most wildly used algorithms in control system at present, has been used in the industrial process control loops. If the PID tuning procedure merely depends on the manual operation, it would be troublesome and time-consuming. And the control results would mainly rely on the experience and knowledge of the operator, which leads to many problems in industrial field application. Therefore, the researches on self-tuning of PID parameters would be meaningful for both scientific researchers and field engineers. To solve above questions, both in the theory and application about PID auto-tuning technique have been researched. The PID auto-tuning control algorithm based on GA and BPNN is proposed in this paper. The total optimization ability is the merit Genetic algorithms. The BP neural network has the advantages of strong nonlinear approximation ability，but easily gets into local dinky value. When two methods combined, the study efficiency of BPNN weights can be improved, the possibilities of sinking into the local solutions can be reduced. The global convergence can be achieved quickly, and the optimal parameters controller will be obtained. Then, the software and simulation research of the studied algorithm is developed and implemented by the MATLAB simulation software. The developed software includes: the conventional PID control, the PID control Optimized by GA, PID controller of BP nerve network and the PID control based on GA and BP neural network. The simulation results show that GABPNN-PID algorithm can not only speed up the convergence speed in the training process , but also adjust the PID controller parameters, for it has more strong capabilities of adaptive and self-study. So the performance of the controller is improved again. Then, the system of the thermostatic temperature water container is designed. And the design process of hardware is described, and the PC software has been developed with VC++ development tools. Finally, the studied algorithms are measured by using the system, and the feasibility of practical application of the PID auto-tuning algorithm based on GA and BPNN has been verified through experiments.