On-line Estimation Research Articles

Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators

Stochastic gradient descent algorithm (SGD) has been popular in various fields of artificial intelligence as well as a prototype of online learning algorithms. This article proposes a novel and general framework of one-sided testing for streaming data based on SGD, which determines whether the unknown parameter is greater than a certain positive constant. We construct the online-updated test statistic sequentially by integrating the selected batch-specific estimator or its opposite, which is referred to opposite online learning. The batch-specific online estimators are chosen strategically according to the proposed sequential tactics designed by two-armed bandit process. Theoretical results prove the advantage of the strategy ensuring the distribution of test statistic to be optimal under the null hypothesis and also supply the theoretical evidence of power enhancement compared with classical test statistic. In application, the proposed method is appealing for statistical inference of one-sided testing because it is scalable for any model. Finally, the superior finite-sample performance is evaluated by simulation studies.

Multiple-Frequency Force Estimation of Controlled Vibrating Systems with Generalized Nonlinear Stiffness

An on-line estimation technique of multiple-frequency oscillatory forces combined with the Hilbert–Huang transform for an important class of actively controlled, forced vibrating mechanical systems with nonlinear stiffness forces is proposed. Polynomial parametric nonlinearities are incorporated in the significantly perturbed vibrating system dynamics. This class of nonlinear vibrating systems can exhibit harmful large-amplitude vibrations, which are inadmissible in many engineering applications. Disturbing oscillations can be also provoked due to interactions of the primary mechanical system to be actively protected against dangerous vibrations with other forced uncertain multidegree-of-freedom nonlinear vibrating systems. Taylor’s series expansion to dynamically model uncertain vibrating forces into a small time window for real-time estimation purposes is employed. Intrinsic mode functions of multiple-frequency vibrating forces can be then obtained by the Hilbert-Huang transform. Uncertain instantaneous frequencies and amplitudes of disturbing oscillations can be directly computed in temporal space. An active vibration control scheme for efficient and robust tracking of prescribed motion reference profiles based on multiple frequency force estimation is introduced as well. The presented closed-loop on-line estimation technique can be extended for other classes of nonlinear oscillatory systems. Analytical, experimental and numerical results to prove the estimation effectiveness are presented. Numerical results show reasonable estimation errors of less than 2%.

Self-organized cascade collaborative optimization method for associated unit processes

The process manufacturing industry includes multiple continuous unit processes, which cooperate with each other to complete a specific production task. Owing to the coupling between processes, it is often difficult to optimize a unit process to achieve global optimization. Considering the influence of the energy-mass coupling between the associated unit processes and the self-organized motion in the system on the process optimization operation, this study proposes a self-organized cascade collaborative optimization method for associated unit processes. By gradually producing a description of the operating status and coupling relationship of the system, a global optimization model can be constructed. First, reaction efficiency is proposed to evaluate the operating status of the system, and an online estimation model is constructed. Second, self-organized criticality is introduced to describe the evolution of the operating status in the collaborative production unit, and the mechanism knowledge and production data characteristics are combined to construct a coupling relationship model describing coupling and collaboration in the cascade system. Finally, a global optimization method for the system is constructed according to the coupling relationship between the associated unit processes and the cascading characteristic of the cascade system and within the unit process. The experimental results show that the method can realize global optimization of the cascade system, which improves the economic benefits compared with the existing production conditions and ensures stability of the system.

Adaptive Constant-Current/Constant-Voltage Charging of a Battery Cell Based on Cell Open-Circuit Voltage Estimation

This paper presents the novel design of a constant-current/constant-voltage charging control strategy for a battery cell. The proposed control system represents an extension of the conventional constant-current/constant-voltage charging based on the so-called cascade control system arrangement with the adaptation of the battery charging current based on the open-circuit voltage parameter estimation. The proposed control strategy features two feedback controllers of the proportional-integral type responsible for: (i) controlling the battery open-circuit voltage towards its fully charged state, and (ii) simultaneously limiting the battery terminal voltage to avoid the battery terminal voltage constraint violation. The open-circuit voltage on-line estimation is implemented by using the system reference adaptive model approach to estimate the linear time-invariant battery equivalent circuit model parameters, whose asymptotic convergence is guaranteed according to Lyapunov stability theory. The proposed concept of the battery charging control is verified by means of simulations using the experimentally obtained model of a lithium iron phosphate battery cell, and it is also compared to other charging methods with respect to charging speed-up potential compared to conventional charging. The proposed method, which can be easily extended to conventional chargers, results in 23.9% faster charging compared to conventional charging, thus representing an inexpensive and straightforward upgrade to conventional battery charging systems.

Autonomous reliable intelligent control design under condition monitoring mechanism: Applied to hypersonic flight vehicles

For the six-degree of freedom dynamics of hypersonic flight vehicle, an autonomous reliable intelligent controller using online estimation and condition monitoring is designed in this paper. Based on model transformation, the outer-loop trajectory tracking is converted to the inner-loop attitude tracking. According to the aerodynamic model of large flight envelope and the thrust model of combined engine, the original dynamics is transformed into a switched nonlinear form. For each subsystem, neural network and nonlinear observer are employed to deal with aerodynamic uncertainty and external disturbance, while modeling errors are built to design composite update laws. Considering that the statically unstable HFV is prone to large deviation due to mode switching and wind disturbance, the condition monitoring signal with finite-time robust design is introduced to adjust control surfaces. With the average dwell time on the switching signal, the stability of the whole switched system is proved using Lyapunov approach. Simulation studies are performed to show the effectiveness of the design.

Estimation of Stator and Magnet Temperatures of IPMSM From Active and Reactive Energies at Medium and High Speeds

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interior Permanent magnet synchronous machines have a wide range of applications, such as in electric vehicles, home appliances, and robots, because of their high torque density and wide operating speed range. In the case of machines, the stator winding and magnet temperatures are generally crucial to guarantee the system’s safety and control accuracy. This study developed a method to estimate magnet and stator winding temperature by analyzing reactive and active energies. First, IPMSM Model involving stator winding and magnet temperatures was derived. In the derived IPMSM model with temperatures, a relationship between reactive energy and magnet temperature was found. Based on the relationship, magnet temperature can be estimated regardless of stator resistance information. Stator winding temperature is estimated based on the active energy by using the estimated magnet temperature. As a result, the proposed method can estimate magnet and stator temperatures simultaneously, considering the speed effect on motor parameters and inductance variation with the magnet temperature. Online estimation was performed to demonstrate the feasibility and validity of the proposed method. Through the online estimation, the maximum estimation errors in magnet and stator temperatures are less than 5 °C and 10 °C, respectively</i> .

Parameter and VSI Nonlinearity Hybrid Estimation for PMSM Drives Based on Recursive Least Square

Precise electrical parameters play important roles in the high-performance control of permanent magnet synchronous machines (PMSMs). This paper proposes a novel parameter and voltage source inverter (VSI) nonlinearity hybrid estimation method to accurately estimate stator resistance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductances, permanent magnet flux linkage, and VSI nonlinearity, in which the effects of magnetic saturation, cross-saturation, and temperature are all considered. The proposed hybrid estimation method consists of two parts: offline and online estimation. In the offline estimation, the four electrical parameters are successively identified by setting different operating conditions, and the identification results are stored in nonvolatile memory in tabular form. In the online estimation, the VSI nonlinearity and the compensation terms of stator resistance and permanent magnet flux linkage related to factors such as temperature and frequency are simultaneously identified by using the recursive least square (RLS) algorithm. Experimental results on a 300 kW PMSM drive system demonstrate that compared to the results achieved with the existing method, the proposed scheme achieves higher estimation accuracy. Consequently, the control performance of the system, such as the output current quality, is efficiently improved.

Fault-Tolerant Self-Tuning Control for Three-Phase Three-Level T-Type Rectifier

This paper proposes a sensorless self-tuning control algorithm for a three-phase T-type rectifier. The proposed sensorless algorithm consists of three layers including an observer, which estimates grid voltages or currents based on the Kalman filter algorithm (KFA), an online estimator, which estimates the state-space matrix parameters, and a linear quadratic tracker (LQT), which is the main controller in this study. On the other hand, the performance of the LQT depends on the gains that need to be adjusted according to the system parameters, which is a challenge. To overcome this problem, a self-tuning method based on recursive least square algorithm (RLSA) is also proposed to tune the gains of LQT online. An experimental prototype is established, and the results, including the steady-state and dynamic performances, are carried out and presented to validate the effectiveness of the proposed controller.

Rotor Position Estimation for Permanent Magnet Synchronous Machines using Electromotive Force and Anisotropy Using Extended-Kalman-Filter

In this paper a new rotor position observer for permanent magnet synchronous machines (PMSM) based on an Extended-Kalman-Filter (EKF) is presented. With this method, just one single EKF is sufficent to evaluate the position information from electromotive force (EMF) and anisotropy. Thus, the PMSM can be controlled for the entire speed range without a position sensor and without the need to switch or synchronize between different observers. The approach covers online estimation of permanent magnetic field and mechanical load. The resulting EKF-based rotor position estimator is embedded in the existing cascaded control concept of the PMSM without need of additional angle trackers or signal filters. The experimental validation for the position sensorless control shows optimized dynamic behaviour.

Multi-stage image registration based on list-mode proton radiographies for small animal proton irradiation: A simulation study

We present a multi-stage and multi-resolution deformable image registration framework for image-guidance at a small animal proton irradiation platform. The framework is based on list-mode proton radiographies acquired at different angles, which are used to deform a 3D treatment planning CT relying on normalized mutual information (NMI) or root mean square error (RMSE) in the projection domain.We utilized a mouse X-ray micro-CT expressed in relative stopping power (RSP), and obtained Monte Carlo simulations of proton images in list-mode for three different treatment sites (brain, head and neck, lung). Rigid transformations and controlled artificial deformation were applied to mimic position misalignments, weight loss and breathing changes. Results were evaluated based on the residual RMSE of RSP in the image domain including the comparison of extracted local features, i.e. between the reference micro-CT and the one transformed taking into account the calculated deformation.The residual RMSE of the RSP showed that the accuracy of the registration framework is promising for compensating rigid (>97% accuracy) and non-rigid (∼95% accuracy) transformations with respect to a conventional 3D-3D registration. Results showed that the registration accuracy is degraded when considering the realistic detector performance and NMI as a metric, whereas the RMSE in projection domain is rather insensitive.This work demonstrates the pre-clinical feasibility of the registration framework on different treatment sites and its use for small animal imaging with a realistic detector. Further computational optimization of the framework is required to enable the use of this tool for online estimation of the deformation.

Inaccuracy Matters: Accounting for Solution Accuracy in Event-Triggered Nonlinear Model Predictive Control

We consider the effect of using approximate system predictions in event-triggered control schemes. These approximations often result from using numerical transcription methods for solving continuous-time optimal control problems. Mesh refinement can guarantee upper bounds on the error in the differential equations that model the system dynamics. We employ the accuracy guarantees of a mesh refinement scheme to show that the proposed event-triggering scheme, which compares the measured system with approximate state predictions, can be used with a guaranteed strictly positive inter-update time. Furthermore, if knowledge of the employed transcription scheme or the approximation errors are available, then better online estimates of inter-update times can be obtained. We also detail a method of tightening constraints on the approximate system trajectory to guarantee constraint satisfaction of the continuous-time system. This is the first work to incorporate prediction accuracy in triggering metrics to guarantee reliable lower bounds for inter-update times and perform solution-dependent constraint tightening.

Fuzzy-Resilient Distributed Optimal Coordination for Nonlinear Multiagent Systems Under Command Attacks

In this paper, we consider the problem of resilient distributed optimal coordination for uncertain nonlinear multi-agent systems, where each agent is remotely dominated by an associated instruction generator via an unreliable network channel which may be distorted by false data injection (FDI) attacks. To avoid evaluating the quality of decision variable or computing its high-order derivatives when applying the backstepping method, which is difficult in a distributed way, a new two-layer integrated design protocol with fuzzy-approximation-based adaptive dynamics compensation mechanism is designed. Through the local online estimation of the optimal solution and design of adaptive feedback gain, the fuzzy adaptive mechanism realizes the mutual compensation of cyber dynamics and physical dynamics. Resultantly, all the agents are shown to achieve the optimal consensus regardless of the FDI attacks and nonlinear uncertainties.

Coestimation of SOC and Three-Dimensional SOT for Lithium-Ion Batteries Based on Distributed Spatial–Temporal Online Correction

Energy storage system based on batteries is a key to achieve green industrial economy and the online estimation of its status is critical for the battery management system. Therefore, this paper proposed a distributed spatial-temporal online correction algorithm for state of charge (SOC) -three-dimensional state of temperature (SOT) co-estimation of battery. Firstly, the internal resistance is identified, and state of charge SOC are estimated based on the adaptive Kalman filter. Then, to improve the fidelity of electrical status estimation under the dynamic operation condition, the SOC estimation is coupled with an online restoration algorithm of distributed temperature. An improved fractal growth process is used to achieve the self-organization and convergence during the restoration of three-dimensional temperature distribution. Finally, to validate the fidelity of online co-estimation algorithm for electrical and thermal parameters, dynamic current profiles are used. The co-estimation method raises the fidelity of SOC estimation by 1.5&#x0025; at most, compared with the SOC estimation algorithm without the SOT estimation. It also keeps the mean relative error of SOT estimation within 8&#x0025;. Additionally, the robustness of the spatial-temporal online correction method with dual adaptive Kalman filters is validated. The result shows that the co-estimation algorithm still has a good converge performance with disturbance added.

Parameter self‐learning feedforward compensation‐based active disturbance rejection for path‐following control of self‐driving forklift trucks

AbstractIn this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC.

ANN-based State of Charge Estimation of Li-ion Batteries for Embedded Applications

The conventional state of charge (SOC) estimation model has several concerns, such as accuracy and reliability. In order to realize robust SOC estimation for embedded applications, this study focuses on three concerns of the existing SOC estimation model: accuracy, robustness, and practicality. In improving the estimation accuracy and robustness, this study took into account the dynamic of the actual SOC caused by the dynamic charging and discharging process. In practice, the charging and discharging processes have characteristics that must be considered to realize robust SOC estimation. The model-based SOC estimation developed based on the virtual battery model causes difficulties for real-time applications. Additionally, model-based SOC estimation cannot be reliably extrapolated to different battery types. In defining the behavior of various types of batteries, the model-based SOC estimation must be updated. Hence, this study utilized data-driven SOC estimation based on an artificial neural network (ANN) and measurable battery data. The ANN model, which has excellent adaptability to nonlinear systems, is utilized to increase accuracy. Additionally, using measurable battery data such as voltage and current signals, the SOC estimation model is suitable for embedded applications. Results indicate that estimating SOC with the proposed model reduced errors with respect to actual datasets. In order to verify the feasibility of the proposed model, an online estimation was out on the embedded system with the use of C2000 real-time microcontrollers. Results show that the proposed model can be executed in an embedded system using measurable battery data.

Development of simplified air drag models including crosswinds for commercial heavy vehicle combinations

Accurate range prediction requires good knowledge of the prevailing wind conditions and how they affect the energy consumption of the ego vehicle. A few different simplified vehicle air drag models that explicitly include the effect from crosswinds are presented and compared through some objective criteria. The models are developed from the normal air drag equation where the effect from wind is implicit and therefore often forgotten or neglected. The purpose is to find a low-complexity model complementing CFD models and wind tunnel tests, that can be used for range estimation and predictive energy management algorithms. To simplify online estimation, a requirement is that the air drag models only contain a few tuning parameters. The models are validated against CFD calculations for a few vehicle combinations and the best models show good accuracy for air attack angles up to at least 60 degrees. It is shown that the parameters of the simplified models can loosely be connected to some basic geometrical attributes of a vehicle combination so it should be possible to give at least a rough estimate of the parameters of a simplified model based on these geometrical attributes. This is useful for making a first estimate of the aerodynamic properties of a vehicle combination after major changes in the exterior, e.g. when adding a trailer. It also highlights that the size and the shape of the vehicle side may be mainly responsible for the high longitudinal air drag sensitivity to crosswinds for large vehicle combinations.

Two-staged approach for estimation of sequences in partially observable P-time Petri nets on a sliding horizon with schedulability analysis

In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools.

Extended state observer-based synergetic control for n-DOF robot manipulators using joint position measurements only

ABSTRACT This research work focuses on the need to provide a control scheme able to tolerate multiple robotic control challenges related to noisy measurements, unknown disturbances and uncertainties using the joint position measurements only. The proposed control scheme avails the advantages of integrating the Extended State Observer (ESO) with the synergetic control to operate n-DOF robot manipulators. The ESO is known as a powerful tool that can provide the online estimation of the uncertainties, nonlinearities and disturbances to be rejected or compensated by a feedback stabilizer. Correspondingly, implementing ESO in the control scheme makes it work with less knowledge of the system model dynamics and states. On the other hand, the synergetic control is a proper controller that can provide the desired tracking performance, but it has a high dependency on the system model which makes it sensitive to control parameters variation and operating conditions. Hence, combining both techniques in one scheme is a convenient approach to overcoming the shortcoming of each method. The proposed method is tested experimentally on the MICO 4-DOF Kinova robot and its performance is compared to the conventional synergetic control considering three case studies to validate its effectiveness.

Online parameter estimation for the McKean–Vlasov stochastic differential equation

We analyse the problem of online parameter estimation for a stochastic McKean–Vlasov equation, and the associated system of weakly interacting particles. We propose an online estimator for the parameters of the McKean–Vlasov SDE, or the interacting particle system, which is based on a continuous-time stochastic gradient ascent scheme with respect to the asymptotic log-likelihood of the interacting particle system. We characterise the asymptotic behaviour of this estimator in the limit as t→∞, and also in the joint limit as t→∞ and N→∞. In these two cases, we obtain almost sure or L1 convergence to the stationary points of a limiting contrast function, under suitable conditions which guarantee ergodicity and uniform-in-time propagation of chaos. We also establish, under the additional condition of global strong concavity, L2 convergence to the unique maximiser of the asymptotic log-likelihood of the McKean–Vlasov SDE, with an asymptotic convergence rate which depends on the learning rate, the number of observations, and the dimension of the non-linear process. Our theoretical results are supported by two numerical examples, a linear mean field model and a stochastic opinion dynamics model.

Global Visual-Inertial Localization for Autonomous Vehicles with Pre-Built Map.

Accurate, robust and drift-free global pose estimation is a fundamental problem for autonomous vehicles. In this work, we propose a global drift-free map-based localization method for estimating the global poses of autonomous vehicles that integrates visual-inertial odometry and global localization with respect to a pre-built map. In contrast to previous work on visual-inertial localization, the global pre-built map provides global information to eliminate drift and assists in obtaining the global pose. Additionally, in order to ensure the local odometry frame and the global map frame can be aligned accurately, we augment the transformation between these two frames into the state vector and use a global pose-graph optimization for online estimation. Extensive evaluations on public datasets and real-world experiments demonstrate the effectiveness of the proposed method. The proposed method can provide accurate global pose-estimation results in different scenarios. The experimental results are compared against the mainstream map-based localization method, revealing that the proposed approach is more accurate and consistent than other methods.