A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived error dynamics and reference trajectory so that the actor neural network can learn the feedforward and feedback control terms at the same time. During the online self-learning process, the actor neural network learns the control policy by minimizing the augmented system’s value function. The input dynamics identified by the recursive least square (RLS) and output of the critic neural network are used to update the actor neural network. In addition, the total uncertainty term of the error dynamics is also identified by RLS, which can compensate for the uncertainty caused by inaccurate modeling, parameter perturbation, and so on. The outputs of ED-DHP include the rough trim surface, feedforward and feedback terms from the actor neural network, and the compensation. Based on this control scheme, the complete knowledge of system dynamics and the reference trajectory dynamics are not needed, and offline learning is unnecessary. To verify the self-learning ability of ED-DHP, two numerical experiments are carried out based on the established morphing air vehicle model. One is sinusoidal signal tracking at a fixed operating point, and the other is guidance command tracking with a morphing process at variable operating points. The simulation results demonstrate the good performance of ED-DHP for online self-learning attitude control and validate the robustness of the proposed scheme

The general power flow algorithm incorporating HVDC system can not consider the diversified control mode of HVDC system. Especially in Power System Analysis Software Package (PSASP), the key control parameters of HVDC are definite, which is to the disadvantage of steady state analysis and actual control of HVDC system. In this paper the control method transformation conditions and dealing strategies were proposed. The power flow calculation mathematic models of HVDC system and controller were established based on user-defined (UD) function of PSASP. The models of tap adjustment of converter transformer and the HVDC control parameters were regarded as a whole joined in PSASP for the power flow calculation. EPRI-36 was taken as an example for detailed calculation and analysis. The result shows that this method is effective and can comprehensively consider HVDC control mode in steady state analysis.

High-performance trajectory-tracking control is essential for high-level automated driving (AD), which demands superior tracking accuracy and robustness to dynamic environmental perturbations. Integrating artificial intelligence (AI) with classical control frameworks offers a potential solution to these challenging demands. This article systematically reviews the latest advances in AI-empowered trajectory tracking, providing a detailed analysis of these achievements from the perspective of control frameworks. We highlight three core AI-empowered modes: AI-enhanced dynamics learning, imitation-guided policy synthesis, and reinforcement-learning–augmented adaptive control. By fusing domain knowledge with data-driven plasticity, these methodologies address critical limitations associated with modeling, parameter optimization, and policy learning. Furthermore, this study evaluates practical deployment barriers, emphasizing simulation-to-reality transitions, safety constraints, and upstream uncertainty management. To advance the field, we outline a research roadmap to develop trajectory tracking from a passive, reactive execution task into a proactive cognitive control paradigm for complex open-world scenarios.