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This article explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (dc), small-scale, electrical network containing an unknown constant power load (CPL). Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback, we consider the incorporation of a controlled dc–dc power converter in parallel with the CPL. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input–output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network. [ABSTRACT FROM AUTHOR]

In this paper we address the challenging problem of designing globally convergent estimators for the parameters of nonlinear systems containing an exponential function whose power depends on unknown parameters. This class of non-separable nonlinearities appears in many practical applications, and none of the existing parameter estimators is able to deal with them in an efficient way. Our main technical contribution is the development of a lifting procedure for non-separable nonlinearly parameterized regressor equations to obtain separable ones, to which we can apply a recently reported estimation procedure. This is illustrated with a human musculoskeletal dynamics problem. The procedure does not assume that the parameters leave in known compact sets, that the nonlinearities satisfy some Lipschitzian properties, nor rely on injection of high-gain or the use of complex, computationally demanding methodologies. Instead, we propose to design a classical on-line estimator whose dynamics is described by an ordinary differential equation given in a compact precise form. [ABSTRACT FROM AUTHOR]

The authors propose a novel decentralised mixed algebraic and dynamic state observation method for multi-machine power systems with unknown inputs and equipped with phasor measurement units (PMUs). More specifically, they prove that for the third-order flux-decay model of a synchronous generator, the local PMU measurements provide enough information to reconstruct algebraically the load angle and quadrature-axis internal voltage. Due to the algebraic structure, a high numerical efficiency is achieved, which makes the method applicable to large-scale power systems. Also, they prove that the relative shaft speed can be globally estimated combining a classical immersion and invariance observer with – the recently introduced – dynamic regressor extension and mixing parameter estimator. This adaptive observer ensures global convergence under weak excitation assumptions that are verified in applications. The proposed method neither requires the measurement of exogenous input signals such as the field voltage and the mechanical torque nor the knowledge of mechanical subsystem parameters. [ABSTRACT FROM AUTHOR]

A position-sensorless algorithm is developed for an interior permanent-magnet synchronous motor, while reflecting the saliency in an extended electromotive force term. Active flux is converted into a new linear regression form, and a high-pass filter was applied. Then, the gradient algorithm is applied to derive an estimate of the active flux in the stationary frame. The proposed observer is an inherently sensorless type because it does not require the rotor speed or position information. It is practically attractive since it does not require the use of pure integrator that is marginally stable. In addition, the observer can be constructed without the phase modulation flux linkage constant. It is basically a model-based method. However, it is easily extended to a signal injection method that is robust in the low-speed region. Ultimate boundedness is established using the persistency of excitation condition. Validity of the algorithm is demonstrated experimentally via torque and speed control with a full load in the low-speed range. [ABSTRACT FROM AUTHOR]

A sensorless algorithm is developed on the basis of a rotor flux observer in the stationary frame. In particular, it involves a parameter adaptive algorithm for an initial rotor flux like the observer, which was recently proposed by Bobtsov et al. (Automatica, vol. 61, Nov. 2015). In the proposed method, the flux observer is linked to the parameter estimator via a compensating term that results from parameter error. This method has a robust property against dc bias errors, i.e., it cures the inherent weakness of the pure integrator (flux observer) to dc offsets that frequently occur in current measurements and voltage estimates. The robust performance is demonstrated through simulations and experimental results. [ABSTRACT FROM AUTHOR]

In this paper we propose a new state observer design technique for nonlinear systems. It consists of an extension of the recently introduced parameter estimation-based observer, which is applicable for systems verifying a particular algebraic constraint. In contrast to the previous observer, the new one avoids the need of implementing an open loop integration that may stymie its practical application. We give two versions of this observer, one that ensures asymptotic convergence and the second one that achieves convergence in finite time. In both cases, the required excitation conditions are strictly weaker than the classical persistent of excitation assumption. It is shown that the proposed technique is applicable to the practically important examples of multimachine power systems and chemical–biological reactors. [ABSTRACT FROM AUTHOR]