In this paper, a novel guidance law based on a reinforcement learning (RL) algorithm is presented to deal with the maneuvering target interception problem using a deep deterministic policy gradient descent neural network. We take the missile’s line-of-sight (LOS) rate as the observation of the RL algorithm and propose a novel reward function, which is constructed with the miss distance and LOS rate to train the neural network off-line. In the guidance process, the trained neural network has the capacity of mapping the missile’s LOS rate to the normal acceleration of the missile directly, so as to generate guidance commands in real time. Under the actor-critic (AC) framework, we adopt the twin-delayed deep deterministic policy gradient (TD3) algorithm by taking the minimum value between a pair of critics to reduce overestimation. Simulation results show that the proposed TD3-based RL guidance law outperforms the current state of the RL guidance law, has better performance to cope with continuous action and state space, and also has a faster convergence speed and higher reward. Furthermore, the proposed RL guidance law has better accuracy and robustness when intercepting a maneuvering target, and the LOS rate is converged.

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 complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor.

This study presents an advanced second-order sliding-mode guidance law with a terminal impact angle constraint, which ingeniously combines reinforcement learning algorithms with the nonsingular terminal sliding-mode control (NTSM) theory. This hybrid approach effectively mitigates the inherent chattering issue commonly associated with sliding-mode control while maintaining high levels of control system precision. We introduce a parameter to the super-twisting algorithm and subsequently improve an intelligent parameter-adaptive algorithm grounded in the Twin-Delayed Deep Deterministic Policy Gradient (TD3) framework. During the guidance phase, a pre-trained reinforcement learning model is employed to directly map the missile’s state variables to the optimal adaptive parameters, thereby significantly enhancing the guidance performance. Additionally, a generalized super-twisting extended state observer (GSTESO) is introduced for estimating and compensating the lumped uncertainty within the missile guidance system. This method obviates the necessity for prior information about the target’s maneuvers, enabling the proposed guidance law to intercept maneuvering targets with unknown acceleration. The finite-time stability of the closed-loop guidance system is confirmed using the Lyapunov stability criterion. Simulations demonstrate that our proposed guidance law not only meets a wide range of impact angle constraints but also attains higher interception accuracy and faster convergence rate and better overall performance compared to traditional NTSM and the super-twisting NTSM (ST-NTSM) guidance laws, The interception accuracy is less than 0.1 m, and the impact angle error is less than 0.01°.

To address the limitations of existing sliding mode-based integrated guidance and control (IGC) schemes, such as chattering, input saturation, and insufficient robustness, this paper proposes a three-dimensional IGC design method incorporating both terminal angle and attitude angle constraints. First, a control-oriented six-degrees-of-freedom model is established based on three-dimensional relative motion and vehicle dynamics, and the control objectives for maneuvering target interception under multiple constraints are clarified. Subsequently, a finite-time terminal sliding mode guidance law based on time-to-go (TGO) is integrated with dynamic surface control to construct the IGC framework. In this design, command filters are introduced to overcome the “explosion of complexity”, while amplitude saturation functions are employed to constrain system states and control inputs. Meanwhile, a generalized super-twisting extended state observer (GSTESO) is incorporated to estimate and compensate for lumped uncertainties in the system. Finally, by combining Lyapunov stability theory with an integral barrier Lyapunov (IBL) function, it is proven that the closed-loop system is uniformly ultimately bounded and satisfies the terminal angle constraints. Comparative simulations under multiple disturbance scenarios demonstrate that the proposed method meets the accuracy requirements in terms of miss distance and LOS angle error. Moreover, it alleviates high-frequency chattering and prevents control-input saturation, showing improved robustness and disturbance rejection capability compared with the baseline methods. Therefore, the proposed approach provides a valuable reference for engineering applications of three-dimensional IGC in maneuvering target interception.