## 1 Introduction  
A networked control system (NCS) is a fully-distributed and networked real-time feedback control system between sensors, actuators and controller signals transmitted over the network [1]. The communication network in the feedback control loop introduces a significant additional delay, especially in complex networks [2]. The time delay can be constant or time varying, which makes the analysis and control design more complex [3]. For the network-induced time-delay compensation problem, many scholars have conducted a great deal of research work. Robust control [4,5], intelligent control [6,7], predictive control [8,9], pinning control [10,11,12] and other time-delay compensation methods have led to a lot of developments and applications.  
Generalized predictive control (GPC) was proposed by [PERSON] in 1987. GPC has many advantages: the first one is based on the parameters of the model; secondly, it retains the advantage of adaptive control and has better robustness than adaptive control; finally, due to the adoption of multi-step prediction, rolling optimization and the feedback correction strategy, the control effect is more suitable for industrial process control requirements.  
The auto regression model is utilized to predict the time delay, and an improved GPC algorithm for time-delay compensation was proposed [13]; however, the author did not give the details of the simulation parameters. Based on a random time-varying network delay, the GPC network based on a state space model is used to access an effective tracking performance [14]. [PERSON] _et al_. gives a predictive control method for a forward channel and feedback channel with random time delay and gives the closed-loop predictive control system stable conditions [15]. However, the GPC algorithm requires solved Diophantine equations and a matrix inversion operation, which led to excessive online calculation; on the other hand, since the time delay is random, the parameter selection for the GPC algorithm requires a larger prediction step size, thereby increasing the calculation time. Furthermore because the network delay is uncertain, the GPC algorithm requires a larger prediction step, thus increasing the computing time, reducing the real-time performance of the system [16].  
Based on the above analysis, the research motivation of this paper includes two aspects. The first is to find a suitable predictive method to predict time delay, to reduce the prediction step size of the GPC algorithm. The second is to improve the GPC algorithm, reducing the calculation amount of the GPC algorithm. This paper will carry out research work on these two aspects. In the literature [17], the author used the particle swarm optimization (PSO) algorithm optimized least squares support vector machine (LS-SVM) to predict the future time delay, compensating time delay through implicit GPC, but the embedding dimension is not optimized in the paper. At the same time, the control value of implicit GPC needs to wait for the next integer time sampling period to be applied to the controlled object. It is easy to cause the system response speed to slow and to produce a big overshoot, so it is necessary to improve it. On the basis of the literature [17], this paper discussed LS-SVM prediction accuracy with different parameters, firstly using the genetic algorithm-optimized parameters of the LS-SVM time-delay prediction model, and then, an improved implicit generalized predictive control method is used to compensate for the random time delay; the output performance of the system is improved. Through the simulation, the effectiveness of the proposed method in this paper is verified.