In order to compensate the network-induced random delays in networked control systems(NCSs), the semi-continuous hidden Markov model(SCHMM) is introduced in this paper to model the controller-to-actuator(CA) delay in the forward network channel. The expectation maximization algorithm is used to obtain the optimal estimation of the model s parameters, and the Viterbi algorithm is used to predict the CA delay in the current sampling period. Thus, the predicted CA delay and the measured sensor-tocontroller(SC) delay in the current sampling period are used to design an optimal controller. Under this controller, the exponentially mean square stability of the NCS is guaranteed, and the SC and CA delays are compensated. Finally, the effectiveness of the method proposed in this paper is demonstrated by a simulation example. Moreover, a comparative example is also given to illustrate the superiority of the SCHMM-based optimal controller over the discrete hidden Markov model(DHMM)-based optimal controller. In order to compensate the network-induced random delays in networked control systems (NCSs), the semi-continuous hidden Markov model (SCHMM) is introduced in this paper to model the controller-to-actuator (CA) delay in the forward network channel The expectation maximization algorithm is used to obtain the optimal estimation of the model s parameters, and the Viterbi algorithm is used to obtain the optimal estimation of the model s parameters, and the Viterbi algorithm is used to predict the CA delay in the current sampling period delay in the current sampling period are used to design an optimal controller. Under this controller, the exponentially mean square stability of the NCS is guaranteed, and the SC and CA delays are compensated. Finally, the effectiveness of the method proposed in this paper is demonstrated by a simulation example. Moreover, a comparative example is also given to illustrate the superiority of the SCHMM-based optimal controller over the discrete hidden Markov model (DHMM) -based optimal controller.