arXiv subjects

Nasser Sadati

Publications and source records attributed to Nasser Sadati.

3 recordsLinked to original sources

arXiv2022

Data Driven Safe Gain-Scheduling Control

Data-based safe gain-scheduling controllers are presented for discrete-time linear parameter-varying systems (LPV) with polytopic models. First, $λ$-contractivity conditions are provided under which safety and stability of the LPV systems are unified through Minkowski functions of the safe sets. Then, to bypass the requirement to identify the system dynamics, a data-based representation of the closed-loop LPV system is provided to directly exploit collected data and construct a safe controller. It is shown that weaker data richness requirements are needed to directly learn a closed-loop safe control policy than to identify the LPV system. The closed-loop data-based representation is leveraged to directly design data-driven gain-scheduling controllers that guarantee $λ$-contractiveness, and, thus, invariance of the safe sets. It is also shown that the problem of designing a data-driven gain-scheduling controller for a polyhedral (ellipsoidal) safe set amounts to a linear program (a semi-definite program). A simulation example is provided to show the effectiveness of the presented approach.

eess.SY↗

arXiv2022

Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net

The control of nonlinear systems with unknown dynamics has been a significant field of research for many years. This paper presents a novel data-driven optimal adaptive control structure with less control effort and faster adaptation than standard adaptive control counterparts. The proposed control structure utilizes the system's recorded data to increase the speed of adaptation and performance dramatically. In this study, we employ a conditional generative adversarial net (CGAN) as a novel central pattern generator to reproduce the steady-state harmonic pattern of the control signals matching the system's uncertainties over a wide range. We can also use the CGAN architecture as a fault detector. The CGAN provides a low-dimensional latent space of uncertainties. It enables rapid and convenient adaptation when there are many parametric uncertainties, especially for large-scale systems. Then, we introduce a novel meta-reinforcement learning framework to adapt the latent space of CGAN to the system's uncertainties as an optimal direct adaptive controller without any system identifier. Another part of the control structure is a regulator that achieves semi-global asymptotic tracking using the Lyapunov stability analysis. Finally, via some simulations, we evaluate the capabilities of the proposed designs on two dynamical systems, a robot manipulator and a large-scale musculoskeletal structure, in the presence of disturbance and perturbation.

eess.SY↗

arXiv2021

CASU2Net: Cascaded Unification Network by a Two-step Early Fusion for Fault Detection in Offshore Wind Turbines

This paper presents a novel feature fusion-based deep learning model (called CASU2Net) for fault detection in offshore wind turbines. The proposed CASU2Net model benefits of a two-step early fusion to enrich features in the final stage. Moreover, since previous studies did not consider uncertainty while model developing and also predictions, we take advantage of Monte Carlo dropout (MC dropout) to enhance the certainty of the results. To design fault detection model, we use five sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors. The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency of each sensor on others which considerably increases the performance of fault detection model. A 10-fold cross-validation approach is used to verify the generalization of the model and evaluate the classification metrics. To evaluate the performance of the model, simulated data from a benchmark floating offshore wind turbine (FOWT) with supervisory control and data acquisition (SCADA) are used. The results illustrate that the proposed model would accurately disclose and classify more than 99% of the faults. Moreover, it is generalizable and can be used to detect faults for different types of systems.

eess.SY↗