A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration method is developed. Considering that the SGD algorithm has slow convergence rates and is sensitive to the step size, a robust and accelerative SGD (RA-SGD) algorithm is derived. This algorithm is based on the Aitken acceleration method, and its convergence rate is improved from linear convergence to at least quadratic convergence in general. Furthermore, the RA-SGD algorithm is always convergent with no limitation of the step size. Both the convergence analysis and the simulation examples demonstrate that the presented algorithm is effective.

This paper presents a new approach for the inclusion of human expert cognition into autonomous trajectory planning for unmanned aerial systems (UASs) operating in low-altitude environments. During typical UAS operations, multiple objectives may exist; therefore, the use of multicriteria decision aid techniques can potentially allow for convergence to trajectory solutions which better reflect overall mission requirements. In that context, additive multiattribute value theory has been applied to optimize trajectories with respect to multiple objectives. A graphical user interface was developed to allow for knowledge capture from a human decision maker (HDM) through simulated decision scenarios. The expert decision data gathered are converted into value functions and corresponding criteria weightings using utility additive theory. The inclusion of preferences elicited from HDM data within an automated decision system allows for the generation of trajectories which more closely represent the candidate HDM decision preferences. This approach has been demonstrated in this paper through simulation using a fixed-wing UAS operating in low-altitude environments.

This paper proposes a U-Model-Based Two-Degree-of-Freedom Internal Model Control (UTDF-IMC) structure with strength in nonlinear dynamic inversion, and separation of tracking design and robustness design. This approach can effectively accommodate modeling error and disturbance while removing those widely used linearization techniques for nonlinear plants/processes. To assure the expansion and applications, it analyses the key properties associated with the UTDF-IMC. For initial benchmark testing, computational experiments are conducted using MATLAB/Simulink for two mismatched linear and nonlinear plants. Further tests consider an industrial system, in which the IMC of a Permanent Magnet Synchronous Motor (PMSM) is simulated to demonstrate the effectiveness of the design procedure for potential industrial applications.

This study presents the fundamental concepts and technical details of a U-model-based control (U-control for short) system design framework, including U-model realisation from classic model sets, control system design procedures, and simulated showcase examples. Consequently, the framework provides readers with clear understandings and practical skills for further research expansion and applications. In contrast to the classic model-based design and model-free design methodologies, this model-independent design takes two parallel formations: (1) it designs an invariant virtual controller with a specified closed-loop transfer function in a feedback control loop and (2) it determines the real controller output by resolving the inverse of the plant U-model. It should be noted that (1) this U-control provides a universal control system design platform for many existing linear/nonlinear and polynomial/state-space models and (2) it complements many existing design approaches. Simulation studies are used as examples to demonstrate the analytically developed formulations and guideline for potential applications.