An Application of Immersion and Invariance to Transient Stability and Voltage Regulation of Power Systems with Unknown Mechanical Power

International audienc

An Application of Immersion and Invariance to Transient Stability and Voltage Regulation of Power Systems with Unknown Mechanical Power

International audienc

Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer

International audienc

Real-time speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque

International audienceIn this paper, an algorithm for direct speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque is described and validated with experimental results. This method is based on the variable structure theories and is potentially useful for adjusting online the induction motor controller unknown parameters (load torque and rotor resistance). The presented nonlinear compensator provides voltage inputs on the basis of rotor speed and stator current measurements, and generates estimates for both the unknown parameters and the nonmeasurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Experiments show that the proposed method achieved very good tracking performance within a wide range of the operation of the induction motor with online variation of the rotor resistance: up to (87%). This high tracking performance of the rotor resistance variation demonstrates that the proposed adaptive control is beneficial for motor efficiency. The proposed algorithm also presented high decoupling performance and very interesting robustness properties with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects, and parameter uncertainties (e.g., inaccuracies on motor inductance values). The other interesting feature of the proposed method is that it is simple and easily implementable in real time. Comparative results have shown that the proposed adaptive control decouples speed and flux tracking while standard field-oriented control does not

Chaos recognition using a single nonlinear node delay-based reservoir computer

Chaotic dynamics are abundantly present in nature as well as in manufactured devices. While chaos in some systems is an undesired phenomenon, in others, they are advantageous because of several applications. Therefore, there is an interest in developing accurate and robust tools for detecting chaos in systems. When the equations describing the system are known, the largest Lyapunov exponent method is used to classify regular from chaotic dynamics. However, when analyzing a process, it often happens that the exact form of the underlying equations is not known. Therefore, it is important to have tools allowing chaos detection using only the time series generated by the theoretical or experimental systems. In this paper, we propose an approach using the single nonlinear node delay-based reservoir computer to separate regular from chaotic dynamics. We show that its classification capabilities are robust with an accuracy of up to 99.03%. We also study the effect of the length of the time series N on the performance of our approach and demonstrate that high accuracy is achieved with short time series ($$N \ge 20$$). Moreover, we demonstrate that the reservoir computer trained with the standard map can classify the dynamical state of another system (for instance, the Lorenz system)

Delay-based reservoir computing using Mackey–Glass oscillator and Arduino board for edge intelligence applications

This article introduces the experimental demonstration of the Mackey–Glass oscillator (MGO)/Arduino-based reservoir computing system as a novel versatile platform for several applications. Performance evaluations conducted on benchmark prediction tasks demonstrate the system’s capabilities with exceptional normalized mean square error (NMSE) values of up to 0.050 [log10(NMSE) ≃ −1.29] for Santa Fe and 0.0034 [log10(NMSE) ≃ −2.46] for electrocardiogram tasks. In addition, we achieve outstanding classification accuracy of up to 96.67% in the chaos recognition task. Our MGO/Arduino-based reservoir computing approach offers many advantages, such as cheapness, affordability, accessibility, and versatility, positioning it as a valuable and efficient solution in advancing neuromorphic computing for edge intelligence applications