Disturbance-Observer-Based U-Control (DOBUC) for Nonlinear Dynamic Systems.

U-model, which is a control-oriented model set with the property of generally facilitate nonlinearity dynamic inversion/cancellation, has been introduced to the Disturbance Observer-Based control (DOBC) methods to improve the performance of the nonlinear systems in this paper. A general DOB based U-Control (DOBUC) framework is proposed to improve the disturbance attenuation capability of U-controller for both linear and nonlinear systems combined with (based on) the U-model-based dynamic inversion which expands the classical linear disturbance observer control to general nonlinear systems. The proposed two-step DOBUC design procedures in which the design of DOB and U-controller are totally independent and separated, enables the establishment of global exponential stability without being subject to disturbances and uncertainties. Comparative simulation experiments with Nonlinear DOBC in controlling Wind Energy Conversion Systems (WECS) and Permanent Magnet Synchronous Motors (PMSM) demonstrated the proposed method.

U-Model-Based Two-Degree-of-Freedom Internal Model Control of Nonlinear Dynamic Systems.

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.

Robust Standard Gradient Descent Algorithm for ARX Models Using Aitken Acceleration Technique.

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.

A composite feedback approach to stabilize nonholonomic systems with time varying time delays and nonlinear disturbances.

In this work, we propose a robust stabilizer for nonholonomic systems with time varying time delays and nonlinear disturbances. The proposed approach implements a composite nonlinear feedback structure in which a linear controller is designed to yield a fast response and a nonlinear feedback control law is considered to increase the system's damping ratio. This structure results in the simultaneous improvement of the steady-state accuracy and transient performance of time-delay nonholonomic systems. Asymptotic stability of the proposed feedback control approach is derived using a Lyapunov-Krasovskii functional aimed at reaching a compromise between system's transient performance and asymptotic stability. Simulation and analytical results are considered to highlight the robustness and superior performance of the proposed approach in controlling high-order-time-delay nonholonomic systems with nonlinear disturbances.

Embedding Human Expert Cognition Into Autonomous UAS Trajectory Planning.

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.