###### Abstract  
This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations. Accepted: 23 November 2022  
12 December 2022  
**Published: 14 December 2022**  
to [9], an adaptive robust control scheme with dead zone compensation was proposed, which was applied to synthesize the system controller for both accurate parameter estimation and guaranteed robust performance to various uncertainties. Ref. [10] investigated the output tracking problem for uncertain nonlinear systems in the presence of actuator dead zone nonlinearity with piecewise time-varying parameters. Technically, a better enhancement of the system performance was achieved by a modified tuning functions approach. In [11], a fuzzy logic dead zone compensator was adopted based on the estimation of the dead zone width that was obtained by using an adaptive method to manage the non-symmetric dead zone input phenomenon. In addition, friction nonlinearity inevitably exists in those contact-type mechanisms, which usually results in sliding and crawling of the mechanism, and greatly affects the stability of the system [12]. It is usually laborious to establish a mathematical model of friction nonlinearity in a practical system because of its special nonlinearity. The Stribeck friction model [13] is widely applied in practical system modeling studies, which can reflect the relationship between relative speed and friction coefficient well, and can describe the friction phenomena well such as static friction, the Stribeck effect and so on. Moreover, it is convenient for identifying the parameters of the Stribeck friction model. Ref. [13] drew the conclusion through a series of experiments that the friction force changed continuously during the process from static to moving. At low speed, the friction torque decreased with the increase in angular velocity. Concerning the friction compensation of the control system, work [14] designed an adaptive steering control system with a friction compensator for an unmanned surface vehicle (USV), where the radial basis function (RBF) neural network was integrated to approximate the friction disturbance online. An adaptive extended state observer (AESO) based robust controller was proposed in [15] for precise position tracking control of a magnetic rodless cylinder with strong static friction, where the AESO with dynamic gains was utilized to estimate the static friction and nonlinearities. There is no doubt that the combination of the dead zone and friction nonlinearity brings greater challenges to designing the controller for implementing the high-precision control of the radar system.