A Lepskiĭ-type stopping rule for the covariance estimation of multi-dimensional Lévy processes

We suppose that a Lévy process is observed at discrete time points. Starting from an asymptotically minimax family of estimators for the continuous part of the Lévy Khinchine characteristics, i.e., the covariance, we derive a data-driven parameter choice for the frequency of estimating the covariance. We investigate a Lepskiĭ-type stopping rule for the adaptive procedure. Consequently, we use a balancing principle for the best possible data-driven parameter. The adaptive estimator achieves almost the optimal rate. Numerical experiments with the proposed selection rule are also presented.

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma-wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e., the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Design of Adaptive Estimator for Nonlinear control system in Noisy Domain

In the recent research studies, adaptive control has been used to monitor a reference control system that has been selected by the researcher. The acceptable reference system can be executed with the presence of nonlinearity and undesirable switches, as they have been disabled. This research study examines the challenges in adapting the state and time-varying parameters, when noise and state disturbances are present in a nonlinear control system. Additionally, this adaptive controller points out the best or most accurate option based on recursive least squares calculations by using the sensor data. This convergence has a rate of arrival with a set time of origin that serves as an estimate for error estimation. In addition, this research work has evaluated the amount of variance in the estimate, which has been caused by external disturbances using adaptive estimators from the reference value present in the noisy domain. The findings obtained via simulation demonstrate the performance of the adaptive estimator through the difference obtained between the reference value and observed value.

Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems

In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The performance of the tolerant estimator was compared with the conventional adaptive estimator, and the tolerant estimator showed accurate estimations against model uncertainty in complicated measurement environments.

Pitch angle control with fault diagnosis and tolerance for wind turbine generation systems

To enhance the reliability of wind turbine generation systems that are generally located in the remote area and subjected to harsh environment, we design the pitch angle control for variable speed wind turbines with the function of fault diagnosis and fault tolerance. The main fault targeted in this research is the mechanical wear and possible break of the blade, pitch gear set or shaft, which cause shaft rotary friction change. The proposed method uses a disturbance observer to diagnose the fault. The estimated fault is used for component assessment and later maintenance. The fault-tolerant control is achieved using a full-order terminal sliding mode control combined with an adaptive neural network estimator. With the compensation of the adaptive estimator, the post-fault states can be driven onto the sliding surface and converge to a small area around the origin. The full-order terminal sliding mode control ensures the state convergence in finite time. The Lyapunov method is used to derive the control law, so that the closed-loop post-fault stability and the convergence of the adaptive estimator adaptation are both guaranteed. The computer simulations of the pitch angle control based on a 5-MW variable-speed variable-pitch angle wind turbine model are conducted with different types of fault simulated. A third-order nonlinear state space model with fault term is derived, and real physical parameters are applied in the simulations. The simulation results demonstrate the feasibility and effectiveness of the proposed scheme and the potential of real-world applications.

System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter

This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty. Uncertainty in the load resistance and the input voltage represent the big challenge in buck converter control. In this work, an adaptive estimator for matched and mismatched uncertainties based backstepping control is applied for DC-DC buck converter. The updating laws are determined based on the lyapunov theorem. Thus, the difference between the estimated parameters and actual parameters converges to zero. The proposed control method is compared with the conventional sliding mode control and integral sliding mode control. Simulation results demonstrate the effectiveness and robustness of the proposed controller.

Parameter Estimation and Target Detection of Phased-MIMO Radar Using Capon Estimator

Phased-Multiple Input Multiple Output (PMIMO) radar is multi-antenna radar that combines the main advantages of the phased array (PA) and the MIMO radars. The advantage of the PA radar is that it has a high directional coherent gain making it suitable for detecting distant and small radar cross-section (RCS) targets. Meanwhile, the main advantage of the MIMO radar is its high waveform diversity gain which makes it suitable for detecting multiple targets. The combination of these advantages is manifested by the use of overlapping subarrays in the transmit (Tx) array to improve the performance of parameters such as angle resolution and detection accuracy at amplitude and phase proportional to the maximum number of detectable targets. This paper derives a parameter estimation formula with Capon's adaptive estimator and evaluates it for the performance of these parameters. Likewise, derivation for expressions of detection performance such as the probability of false alarm and the probability of detection is also given. The effectiveness and validation of its performance are compared to conventional estimator for other types of radars in terms of the effect of the number of target angles, the RCS of targets, and variations in the number of subarrays at Tx of this radar. Meanwhile, the detection performance is evaluated based on the effect of Signal to Noise Ratio (SNR) and the number of subarrays at Tx. The evaluation results of the estimator show that it is superior to the conventional estimator for estimating the parameters of this radar as well as the detection performance. Having no sidelobe makes this estimator strong against the influence of interference and jamming so that it is suitable and attractive for the design of radar systems. Root mean square error (RMSE) on magnitude detection from LS and Capon estimators were 0.033 and 0.062, respectively. Meanwhile, the detection performance for this radar has the probability of false alarm above 10-4 and the probability of detection of more than 99%.

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.