## 1 Introduction  
The complexity of the dynamic system makes it generally urgent to develop process control technology. Numerous control methods have been proposed, such as fuzzy control [1], neural control [2] and adaptive control [3]. Among these, the proportion integration differentiation (PID) controller is the most popular. Thanks to its simple structure, easy implementation and robust performance, the PID controller was widely used in many different fields, including process control [4], flight control [5], motor drives [6] and instrumentation [7]. Modern industrial systems often suffered from problems of high orders, nonlinearities and time delays [8, 9]. In order to improve the control accuracy of systems, many intelligence methods were researched and compared [10]. The main object of the PID controller is to adjust the response signals to be as close as possible to the drive signals. Thus, it is important to optimally adjust the controller's parameters.  
The past decades have witnessed the proposal of many methods to improve the capacity of parameters' optimization. The first classical tuning method was proposed by [PERSON](Z-N). However, the performance of Z-N on the practical engineering was poor [11]. In recent decades, artificial intelligence algorithms have developed and play an important role in optimization fields, such as artificial neural network (ANN) [12], genetic algorithm (GA) [13], fuzzy logic [1] and neural-fuzzy system [2]. Due to the poor performance of the Z-N method, intelligence algorithms were put into use for tuning the PID controller. The above theoretical tuning methods were proven to have better applications in acquiring optimal parameters.  
Particle swarm optimization (PSO) was an evolutionary computation derived from the behavior of birds flocking, insects swarming and fish schooling [14]. All particles were updated by their positions and velocities during iterations [15]. The optimal solution was searched for in particles' cooperation and information sharing. Thanks to its high capacity for acquiring the optimal solution within shorter iteration time and stable convergence property, PSO was widely applied in the areas of system identification [16], feature selection [17], function optimization [18], neural network training [19] and fuzzy systems control [20]. Thus, PSO is an excellent method for solving the problem of self-tuning the PID controller. As for the particular weaknesses and advantages of different algorithms, many researches proposed the theory of algorithm fusion to increase the optimization efficiency [21; 22]. GA had outstanding global search ability on account of the genetic operation [23]. Similar to other optimization algorithms, PSO was easily trapped in the local optimum [24; 25]. In order to improve the global search ability and convergence characteristics, this paper combined GA and PSO for self-tuning the PID controller. The simulation and experiment were conducted on a hydraulic cylinder pressure system. The results showed the obvious improvement in optimizing the parameters of the PID controller.  
The paper is organized as follows. Section 2 describes the related work about the PID controller and PSO algorithm. The basic theories of the PID controller and PSO are presented in Section 3. The specific process for tuning PID parameters and the flow of pressure control for the hydraulic cylinder are described in Section 4. Section 5 shows the simulation and experiment results. Finally, a brief conclusion is illustrated in Section 6.