In this article, a novel finite-time convergent three-dimensional terminal sliding mode guidance law is proposed for intercepting maneuvering targets in three-dimensional space with terminal angle constraints. The proposed guidance law introduces a novel generalized super-twisting extended state observer (GSTESO) to estimate the maneuvering target’s acceleration and lumped disturbances, enabling quicker convergence to the true values and offering better noise tolerance. Moreover, a time-varying function called time base generator (TBG) is introduced in the design of the sliding surface, forming a new terminal sliding mode function that ensures that the line-of-sight (LOS) angle converges within a small neighborhood of the desired value at interception. It also offers good robustness and higher guidance accuracy, effectively avoiding overload saturation in the initial stages of guidance. Simulation results indicate that the proposed TBG-based finite-time terminal sliding mode (TBGFTTSM) guidance law can reduce overload magnitude and ensure continuous and smooth guidance commands, while the performance of the GSTESO is also validated.

In order to improve mathematical model accuracy of electromechanical actuator (EMA) and solve the problems of low-frequency response and large overshoot for nonlinear systems by using traditional proportional integral derivative (PID) algorithm, a fuzzy single neuron (FSN)-PID algorithm is proposed. Firstly, a complete multi-nonlinear dynamic model of EMA is constructed, which introduces internal friction and current limiter of brushless direct current motors (BLDCMs), dead zone backlash of gear trains, and LuGre friction between output shaft and fin. Secondly, a FSN-PID controller is introduced into the automatic position regulator (APR) of EMA control system, where the gain coefficient K of SN algorithm is adjusted by fuzzy control, and the stability of the controller is proved. In addition, simulations are conducted on the response effect of different fin positions under different algorithms for the analysis of the 6° fin position response; it can be concluded that the rise time with FSN-PID algorithm can be reduced by about 4.561% compared to PID, about 1.954% compared to fuzzy (F)-PID, about 0.875% compared to single neuron (SN)-PID, and about 0.380% compared to back propagation (BP)-PID. For the 4°-2 Hz sine position tracking analysis, it can be concluded that the minimum phase error of FSN-PID algorithm is about 0.4705 ms, which is about 74.44% smaller than PID, about 73.43% smaller than F-PID, about 17.24% smaller than SN-PID, and about 10.81% smaller than BP-PID. Finally, wind tunnel experiments investigate the actual high dynamic flight environment and verify the excellent position tracking ability of FSN-PID algorithm.

An increasing number of applications of the electromechanical actuator (EMA) in the flight vehicle control system have required accurate dynamic models and control strategies. This paper investigates an EMA system for guided projectiles characterized by a high power-to-weight ratio, high efficiency, and high integration. First, the working principle of the EMA is analyzed, along with the structural composition of its mechanical and electrical components. A detailed nonlinear dynamic model of the EMA is established, and internal nonlinearities such as friction and backlash are investigated. Subsequently, four intelligent controllers, namely, the fuzzy adaptive (FA)-proportional integral derivative (PID), single neuron (SN)-PID, back propagation (BP)-PID, and radial basis function (RBF)-PID, are designed and implemented in the automatic position regulator of the three-closed-loop EMA control system. In addition, simulations are conducted to compare the response tracking performance of the EMA for small angle step and sine displacement under different controllers. The results indicate that with the RBF-PID controller, the rise time of the 1° step displacement response is ∼318.950 ms, representing reductions of about 0.626%, 0.674%, 0.795%, and 0.981% compared with the BP-PID, SN-PID, FA-PID, and conventional PID controllers, respectively. Moreover, with the RBF-PID controller, the amplitude error (AE) in tracking the 1°-1 Hz sine displacement is ∼3.65 × 10−3, representing reductions of about 3.865%, 5.276%, 7.454%, and 10.254% compared with the BP-PID, SN-PID, FA-PID, and conventional PID controllers, respectively. Finally, experiments validate the command transmission delay time. Results from small angle step and sine displacement tests demonstrate the rapidity, stability, and robustness of the artificial intelligence strategies.

In the paper, an advanced Gauss pseudospectral method (AGPM) is proposed to estimate the parameters of the continuous-time (CT) Hammerstein model consisting of a CT linear block followed by a static nonlinearity. The basic idea of AGPM is to transcribe the CT identification problem into a discrete nonlinear programming problem (NLP), which can be solved with the well-developed sequential quadratic programming (SQP) algorithm. The nonlinear part of the Hammerstein system is approximated with the Gauss pseudospectral approximation method. The linear part is written as a controllable canonical form. AGPM can converge to the true values of the CT Hammerstein model with few interpolated Legendre-Gauss (LG) nodes. Lastly illustrative examples are proposed to verify the accuracy and efficiency of the method.