Abstract
This paper investigates the design of a missile seeker servo system combined with a guidance and control system. Firstly, a complete model containing a missile seeker servo system, missile guidance system, and missile control system (SGCS) was creatively proposed. Secondly, a designed high-order tracking differentiator (HTD) was used to estimate states of systems in real time, which guarantees the feasibility of the designed algorithm. To guarantee tracking precision and robustness, backstepping sliding-mode control was adopted. Aiming at the main problem of projectile motion disturbance, an adaptive radial basis function neural network (RBFNN) was proposed to compensate for disturbance. Adaptive RBFNN especially achieves online adjustment of residual error, which promotes estimation precision and eliminates the “chattering phenomenon”. The boundedness of all signals, including estimation error of high-order tracking differentiator, was especially proved via the Lyapunov stability theory, which is more rigorous. Finally, in considered scenarios, line of sight angle (LOSA)-tracking simulations were carried out to verify the tracking performance, and a Monte Carlo miss-distance simulation is presented to validate the effectiveness of the proposed method.
Highlights
A radar seeker is the “eye” of a missile, and it’s one of the most important parts of and the biggest sensor in the missile
A radar seeker servo system with platform (RSSSP) is a kind of high-precision servo tracking system, which is mounted on the front of a missile to achieve stable tracking of moving targets, while the object of the controlling system is an inertially stabilized platform (ISP)
From existing studies, this paper combined the RSSSP with missile guidance and control systems to design a control algorithm, and a Monte Carlo simulation was carried out to verify the improvement of guidance precision, which is more realistic than analyzing servo systems by themselves; differently from traditional research in which the reference signal is given as a specific function, this paper applies high-order tracking differentiator (HTD) to estimate system states in real time, and all signals involved were generated in real time; different from traditional radial basis function neural network (RBFNN), this paper proposed an adaptive RBFNN that adjusts the residual error in time, which enhances the estimation precision
Summary
A radar seeker is the “eye” of a missile, and it’s one of the most important parts of and the biggest sensor in the missile. From existing studies, this paper combined the RSSSP with missile guidance and control systems to design a control algorithm, and a Monte Carlo simulation was carried out to verify the improvement of guidance precision, which is more realistic than analyzing servo systems by themselves; differently from traditional research in which the reference signal is given as a specific function, this paper applies HTD to estimate system states in real time, and all signals involved were generated in real time; different from traditional RBFNN, this paper proposed an adaptive RBFNN that adjusts the residual error in time, which enhances the estimation precision. Is the target, there unavoidably existed misalignment angle θ g is the the missile lengthwise axis and horizontal plane, while rotation angle the antenna surface in pitch channel. Θg plus θa is considered as the LOSA, and the changing rate of it is the LOSA rate, which is necessary for the guidance system
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