Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

This paper proposes a design scheme along with stability analysis of a new adaptive backstepping controller designed for permanent magnet synchronous generator-based wind turbine, by using artificial neural network-based uncertainty compensation. The idea is to control the rotor speed and the mechanical power generated under internal and external nonlinear parametric uncertainties. An uncertain model of permanent magnet synchronous generator is designed. Then, two artificial neural network compensators are built to compensate such uncertainties in the current loops. The stability of the closed-loop system is studied according to the Lyapunov function. Simulations of the dynamic model are performed under both variable step and random wind speeds by using the DEV-C++ software, and the results are plotted with MATLAB. Compared to the classical direct torque control technique, the obtained results show the robustness of the proposed controller despite the presence of different uncertainties.

Multiphysics Modelling of Stone Wool Fire Resistance

In fire resistance tests, stone wool’s organic matter undergoes exothermic oxidative reactions sustained by external heat, causing mass transfer in the structure. The previous modelling attempts, lacking the mass transfer physics, fall short in predicting the temperature of high density and high organic content samples. To fill this gap in the fire engineering modelling capability, we include mass transfer in our calculation, and validate the model using experimental fire resistance data. As an alternative, we use a heat conduction -based model lacking the gas transfer but with reaction kinetics coupled to the stone wool’s organic mass %. The results show that the thermal effects of the oxidative degradation can be predicted by introducing the simplified diffusion processes. The oxygen transfer and exothermic reactions depend upon the amount of organic content, and the uncertainty of temperature predictions is $$\pm \,20\%$$ ± 20 % . In average, temperatures and critical times are more accurately predicted by the heat conduction model, while, the peak temperature prediction uncertainty is low ($$\pm \,10\%$$ ± 10 % ) with the multiphysics model. The uncertainty compensation method reduces the difference between the two model predictions. Nevertheless, further validation study is needed to generalize the uncertainty compensation metrics. Finally, we demonstrate how a gas flow barrier on the cold side (sandwich) can effectively reduce the peak temperature of the high organic content-stone wools.

Automatic and fast encoding of representational uncertainty underlies probability distortion

Humans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way (“probability distortion”), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information (“representational uncertainty”) and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior. Human subjects kept tracking the relative frequency of one color of dot in a sequence of dot arrays while their brain activity was recorded by magnetoencephalography (MEG). We found converging evidence from both neural entrainment and time-resolved decoding analysis that a mathematically- derived measure of representational uncertainty is automatically computed in the brain, despite it is not explicitly required by the task. In particular, the encodings of relative frequency and its representational uncertainty respectively occur at latencies of approximately 300 ms and 400 ms. The relative strength of the brain responses to these two quantities correlates with the probability distortion behavior. The automatic and fast encoding of the representational uncertainty provides neural basis for the uncertainty compensation hypothesis of probability distortion. More generally, since representational uncertainty is closely related to confidence estimation, our findings exemplify how confidence might emerge prior to perceptual judgment.

Posture Stabilization of Wheeled Mobile Robot Based on Passivity-Based Robust Switching Control with Model Uncertainty Compensation

Thisstudy presents apassivity-based robust switching control for the posture stabilization of wheeled mobile robots (WMRs) with model uncertainty. Essentially, this proposed strategy is switching between (1) passivity-based robust control to lead the robot to the neighborhood of local minima with a finite time and (2) another robust control to perturb the w-rotational motion of the WMR before the v-kinetic energy of the WMR become meaningless, thereby, eventually converging to the desired posture. Thus, combining two switching control laws ensures the global convergence of (x,y)-navigation of WMRs from any initial position to desired set. Especially, the inter-switching time is intentionallyselected before the WMR completely loses its mobility, which ensures a strict decrease in (x,y)-navigation potential energy and a better global convergence rate. In addition, this control architecture also includes model uncertainty compensation, often neglected in practice, and analytical study of rotational perturbation was also conducted. The Lyapunov technique and energetic passivity wereutilized to derive this control law. Simulation results are presented to illustrate the effectiveness of the proposed technique. It wasfound from the results that the WMR wasquickly converged to the desired posture even under the presence of model uncertainty.

A Model-Free Continuous Velocity Observer Formulation With Self-Tuning for Mechatronic Systems

In this study, the design of a smooth robust velocity observer for a class of uncertain nonlinear mechatronic systems is presented. The proposed velocity observer does not require a priori knowledge of the upper bounds of the uncertain system dynamics and introduces time-varying observer gains for uncertainty compensation. Practical stability of the velocity observation error is ensured via Lyapunov-type stability analysis. Experimental results obtained from Phantom Omni haptic device are presented to illustrate the performance of the proposed velocity observer.