Robust Adaptive Approach Research Articles

An incentive-compatible distributed integrated energy market mechanism design with adaptive robust approach

The transactive energy framework was proposed in the recent years to enable the distributed energy trading among distributed integrated energy market. However, sharing energy might lead to profit loss to some participants. As a result, the incentive of joining the coalition for some participants will be diminished. Currently, researches have focused on encouraging participants to share energy by offering compensation, but the incentive compatibility is not modeled effectively. To address this problem, this paper, for the first time, develops an optimization model for the market organizer (i.e., the integrated energy service provider) to allocate the cooperative surplus according to the market contribution while the crucial attributes of the market mechanism are ensured (i.e., social welfare maximization, individual rationality, cost recovery and incentive compatibility), so that market participants can earn their fair shares and profit reductions can be circumvented. On the other hand, the problem caused by the highly uncertain and intermittent power outputs from distributed renewable energy resources imposes additional challenge for organizing a transactive energy market, which calls for the exploitations of effective algorithms to identify representative scenarios. In this paper, adaptive robust optimization algorithm is adopted to identify the scenarios that enhances conservativeness and economy and improves the efficiency of market clearing process, which transforms the problem into a tri-level model and solves by iterating between internal and external formulations. Simulation results show that social welfare increases $2,751 compared with independent operation mode, and calculation efficiency can be improved by 71.55% utilizing the ARO method.

An adaptive sliding mode observer for inverted pendulum under mass variation and disturbances with experimental validation

In this paper, a new robust adaptive estimation approach is designed for the inverted pendulum. The estimation scheme is composed by an auxiliary high gain observer in cascade with an adaptive sliding mode observer. The auxiliary high gain observer is designed to estimate auxiliary outputs in order to solve the dissatisfaction of observer matching condition related to the presence of disturbances. Then, using the estimated auxiliary outputs, the adaptive sliding mode observer is synthesized to estimate conjointly the states, the unknown parameter (the pendulum mass which appears nonlinearly in the dynamics of the pendulum model) and the unknown disturbances. The stability analysis is established using the Lyapunov approach. Numerical simulations are carried out to validate theoretical results. Furthermore, experimental tests are realized using a real pendulum setup (a product of Feedback Instruments) to highlight the good performances of the proposed estimation method in practice.

Broadband Noise Suppression of Stationary Clocked DC/DC Converters by Injecting Synthesized and Synchronized Cancellation Signals

Active cancellation of disturbing signals is a common method in electromagnetic compatibility (EMC) of power electronic systems. In this paper, a new method of suppressing periodic disturbances is extended and applied. In this method, the disturbing harmonics are suppressed by a synthesized cancellation signal that is synchronized with the converter's operation. Here, the cancellation signal is synthesized from a number of destructive sine waves. The appropriate amplitudes and phases are found via a convenient and robust adaptive approach. As a special feature of this method, many troublesome effects, like delays or complex frequency characteristics, can be compensated easily. Therefore, this method does not suffer from the same limitations as previous active techniques. Until now, the method has only been proven for a small number of harmonics. In this study, it is applied to a wide frequency range of 150 kHz to 30 MHz of a 48-/12-V dc/dc converter, e.g., for automotive applications. An optimization strategy is developed from a causal model of the system. A test setup is realized, and the sensor's and injector's performances are discussed regarding the automotive EMC standard CISPR 25. Measurement results for the artificial network and the antenna are presented. The additional power losses are estimated.

Accurate Optic Disc and Cup Segmentation from Retinal Images Using a Multi-Feature Based Approach for Glaucoma Assessment

Accurate optic disc (OD) and optic cup (OC) segmentation play a critical role in automatic glaucoma diagnosis. In this paper, we present an automatic segmentation technique regarding the OD and the OC for glaucoma assessment. First, the robust adaptive approach for initializing the level set is designed to increase the performance of contour evolution. Afterwards, in order to handle the complex OD appearance affected by intensity inhomogeneity, pathological changes, and vessel occlusion, a novel model that integrates ample information of OD with the effective local intensity clustering (LIC) model together is presented. For the OC segmentation, to overcome the segmentation challenge as the OC’s complex anatomy location, a novel preprocessing method based on structure prior information between the OD and the OC is designed to guide contour evolution in an effective region. Then, a novel implicit region based on modified data term using a richer form of local image clustering information at each point of interest gathered over a multiple-channel feature image space is presented, to enhance the robustness of the variations found in and around the OC region. The presented models symmetrically integrate the information at each point in a single-channel image from a multiple-channel feature image space. Thus, these models correlate with the concept of symmetry. The proposed models are tested on the publicly available DRISHTI-GS database and the experimental results demonstrate that the models outperform state-of-the-art methods.

Smart Wire Placement to Facilitate Large-Scale Wind Energy Integration: An Adaptive Robust Approach

Enhanced utilization of the existing transmission grid is a cheaper and paramount way to have a high penetration of large-scale wind energy resources. Smart wire devices (SWDs) are a new technology to enhance the transfer capability through power flow control. The SWDs are distributed, cheap, self-governing smart assets of FACTS technologies, which can adjust the power flow in an interconnected transmission network. Accordingly, this paper presents a comprehensive three-stage robust SWD placement model, which minimizes the generation and investment costs while guaranteeing that the adaptive and secure robust solution is accustomed to cover the wind uncertainty interval. Since the proposed robust model is not solvable via an off-the-shelf optimization package and to reduce the computation burden of the solution process, an effective solution strategy is proposed to solve it. Detailed simulation results on the IEEE 24-bus system verify the effectiveness of the proposed robust SWD placement model.

Active EMI Reduction of Stationary Clocked Systems by Adapted Harmonics Cancellation

Active cancellation of disturbing signals is a common method in EMC. In this paper, a specialized strategy is presented to minimize the disturbing harmonics of stationary clocked systems by injecting an appropriate harmonic cancellation signal with an adjustable signal synthesizer. The optimum cancellation signal is found via a convenient and robust adaptive approach. Each destructive harmonic is generated individually, and the cancelation signal is the superposition of a set of sinusoidal signals. As a special feature of this method, many troublesome effects, like delays or complex frequency characteristics, can be compensated easily. Several implementation variants can be derived from this general approach. Here, the variant, continuously adapted harmonics cancellation (CAHC), is considered. The system's limitations due to the ADC, the DAC, and the synchronization are described. An FPGA-implementation of CAHC is presented and applied to a dc/dc converter in an automotive component measurement setup to demonstrate the effectivity of the method.

Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios.

On the interplay between robustness and dynamic planning for adaptive radiation therapy**There is no COI or financial disclosure to report.

Interfractional geometric uncertainties can lead to deviations of the actual delivered dose from the prescribed dose distribution. To better handle these uncertainties during the course of treatment, the authors propose a framework for robust adaptive radiation therapy in which a variety of robust adaptive treatment strategies are introduced and evaluated. This variety is a result of optimization variables with various degrees of freedom within robust optimization models that vary in their grade of conservativeness. The different degrees of freedom in the optimization variables are expressed through either time-and-uncertainty-scenario-independence, time-dependence or time-and-uncertainty-scenario-dependence, while the robust models are either based on expected-value-, worst-case- or conditional value-at-risk-optimization. The goal of this study is to understand which mathematical properties of the proposed robust adaptive strategies are relevant such that the accumulated dose can be steered as close as possible to the prescribed dose as the treatment progresses. We apply a result from convex analysis to show that the robust non-adaptive approach under conditions of convexity and permutation-invariance is at least as good as the time-dependent robust adaptive approach, which implies that the time-dependent problem can be solved by dynamically solving the corresponding time-independent problem. According to the computational study, non-adaptive robust strategies may provide sufficient target coverage comparable to robust adaptive strategies if the occurring uncertainties follow the same distribution as those included in the robust model. Moreover, the results indicate that time-and-uncertainty-scenario-dependent optimization variables are most compatible with worst-case-optimization, while time-and-uncertainty-scenario-independent variables find their best match with expected value optimization. In conclusion, the authors introduced a novel framework for robust adaptive radiation therapy and identified mathematical requirements to further develop robust adaptive strategies in order to improve treatment outcome in the presence of interfractional uncertainties.

Estimating neural sources using a worst-case robust adaptive beamforming approach

Recently, brain source localization and beamforming methods have played an important role in enhancing the utility of the electroencephalograph (EEG) and/or the magnetoencephalograph (MEG). Source localization methods are in general very sensitive to parameter values used to describe the underlying lead–field matrix, such as head shape, electrode positions, conductivity of various tissues of the head, etc. Errors in these parameter values can cause significant degradation in performance of these algorithms.In this paper, we develop a robust minimum variance beamformer (RMVB) specifically for EEG applications that can deal with an arbitrary mismatch between the assumed and true lead field matrix. The approach optimizes the worst-case uncertainty performance, sacrificing a distortionless response in exchange for more robust performance. The performance of the RMVB is compared with the classic minimum variance beamformer (MVB) as well as its regularized and eigenspace-based variations. The simulation scenarios highlight the superior ability of the RMVB to cope with arbitrary mismatches.

Adaptive beamforming with unknown scattering coefficients of near‐field scatterers

Conventionally, near-field scattering effect was considered in antenna measurement, which requires a great amount of measuring efforts and re-measurement if the operational platform has changed. In this study, the authors consider the near-field scattering problem in the framework of adaptive array beamforming theory, which avoids re-measurement and can be adaptive to the arbitrary scenario. Generally, the near-field scattering can result in severe performance degradation in adaptive beamforming applications. They propose a robust adaptive beamforming approach that can maintain the performance in the presence of near-field scatterers with unknown scattering coefficients. In the authors’ approach, the near-field scatterering signal component is incorporated into the presumed steering vector. Thus, it is treated as useful information in this study instead of nuisance interference in the literature. In particular, the properties of the far-field direct-path signal and near-field signal are explored and the large uncertainty set is divided into two small ones describing the far-field and near-field steering vectors, respectively. Simulation examples are provided to show the performance improvement by making use of the near-field scattering signals.

Robust adaptive beamforming based on covariance matrix and new steering vector estimation

A new approach to the design of robust adaptive beamforming is introduced. In this approach, first the sample covariance matrix is replaced by the estimated interference-plus-noise covariance matrix which is constructed by exploiting the spatial spectrum distributions of a generalized Hermitian covariance matrix and the sample covariance matrix. Then, a new robust adaptive beamforming approach is developed based on finding a more accurate estimate of the actual steering vector than the available prior. The essence of finding such a steering vector estimate is the construction of the covariance matrix of the signal-of-interest based on the Capon spectrum estimator. The estimator is based on the integration over an angular region in which the desired signal direction is located. In the proposed method, the norm constraints are not required. Furthermore, it avoids solving quadratic convex optimization programs. The effectiveness of the proposed method is demonstrated by numerical results.

Robust adaptive learning approach to self-organizing maps

The Self-Organizing Map (SOM) has attracted much research in diverse fields of study, where it has been successfully applied to a wide range of artificial intelligence applications. In this paper, a new intelligent adaptive learning SOM, in contrast with the conventional SOM algorithm, is presented. The proposed SOM overcomes the disadvantages of the conventional SOM by deriving a new variable learning rate that can adaptively achieve the optimal weights and obtain the winner neurons with a shorter learning process time. To quantify the performance, the proposed SOM was compared with various unsupervised algorithms to examine how well the network weight update equation adapts and reaches the optimum weights with fewer iterations. Extensive experiments were conducted using eight different databases from the UCI and KEEL repositories. The proposed SOM algorithm was further compared with the conventional SOM, GF-SOM, PLSOM, and PLSOM2 algorithms. The proposed SOM algorithm showed superiority in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map, and accuracy during the classification process.

Proactive Management of Microgrids for Resiliency Enhancement: An Adaptive Robust Approach

Deploying microgrids ( $\boldsymbol {\mu G} \text{s}$ ) with the capability of islanding and self-supply can considerably enhance power system resilience in coping with weather events. To this end, practical methods are needed in order to operationally position and manage the $\boldsymbol {\mu G}$ to optimally control its resources and minimize the risk in the face of such disturbances. This paper proposes a two-stage adaptive robust formulation for $\boldsymbol {\mu G}$ predisturbance scheduling to minimize the damaging consequences of islanding events. The approach considers different uncertainties and obtains the best day-ahead schedule for the $\boldsymbol {\mu G}$ , which is robust against realization of uncertain parameters. An appropriate decomposition strategy, called column-and-constraint generation algorithm, is adopted to efficiently solve the problem, and “budget of uncertainty” parameters are introduced to control the conservatism of the robust solution. The effectiveness of the novel framework is evaluated and discussed on a $\boldsymbol {\mu G}$ using a set of illustrative case studies. The simulations show that the framework mitigates the huge cost of load shedding at the expense of a small increase in the predisturbance preparation cost. The advantages of our model are particularly remarkable when a weather event will come through, and a significant level of uncertainty exists.

Robust Adaptive Backstepping Excitation Controller Design for Higher-Order Models of Synchronous Generators in Multimachine Power Systems

A robust adaptive backstepping approach is considered in this paper to design excitation controllers for synchronous generators in power systems. The higher-order dynamical models of synchronous generators are considered to design the proposed excitation controller. Since the parameters appearing within these dynamical models (especially the parameters related to the synchronous generators and excitation systems) significantly affect the stability of power systems, these parameters are considered as completely unknown during the proposed controller design process. The parameter adaptation laws are used in this paper to estimate the unknown parameters while maintaining the overall stability of power systems. The effects of external disturbances are also considered in this paper to incorporate the model accuracies and measurement noises, which in turn ensure the robustness of the proposed control scheme. The control Lyapunov functions are used to analyze the stability of the whole system through the negative semi-definiteness of their derivatives. Simulation studies are carried out on an IEEE standard power system, the New England 10-machine 39-bus power system, to further validate the performance of the proposed scheme under different operating conditions. Comparisons are also made with different existing nonlinear excitation controllers sliding mode controller and robust partial feedback linearizing controller, which are designed based on the higher order models as well as with the robust adaptive controller, which is designed based on the traditional one-axis model. Simulations results, under different operating conditions, demonstrate the superiority of the proposed control scheme over all these existing nonlinear excitation controllers.

Robust adaptive beamforming using iterative variable loaded sample matrix inverse

In the presence of steering vector (SV) mismatch, a novel iterative variable loading robust adaptive beamforming approach is presented in a new perspective. The proposed method searches for the desired array SV iteratively based on the relationship between the optimal weight vector and the assumed SV of standard Capon beamformer, which avoids any Lagrange multiplier methodology or the convex optimisation methods in each iteration. Simulation results demonstrate the superior performance of the proposed method.

Direct Adaptive Compensation Control of Mechanical Systems With Unknown Actuator Failures and Dead-Zone Nonlinearities

In this paper, a new tuning function backstepping control scheme is proposed for a class of parametric strict feedback nonlinear systems to accommodate actuator failures/faults and dead-zone constraints, where the failures/faults are uncertain in time, pattern, and values, and the dead-zone parameters are not available. Roughly speaking, such a scheme is developed in two steps below. First, by using an adaptive smooth inverse function to compensate for the dead-zone nonlinearity, we separate the coupling actuator dynamics into two parts, i.e., the dead-zone compensation errors and the nominal failure dynamics. Afterward, we further handle these two parts based on the techniques of robust adaptive approach and parametrization method. With our scheme, the global boundedness of the signals in the closed-loop system are ensured, and the tracking error is steered to zero asymptotically. These results have also been verified through simulation studies.

An Adaptive Robust Approach to Modeling and Control of Flexible Arm Robots

In this paper, a novel adaptive robust approach to modeling and control of a class of flexible-arm robots subject to actuators unmodeled dynamics is proposed. It is shown how real-time signals measured from a dynamical system can be utilized to improve the accuracy of the mathematical model of flexible robots. Given the elasticity of the robot’s arms, flexible manipulators have both passive and active degrees of freedom. A nonlinear robust controller is designed for the active degrees of freedom to enable the robot to follow desired trajectories in the presence of actuators unmodeled dynamics. Furthermore, it is shown that under some feasible conditions, another nonlinear robust controller is designed for the passive degrees of freedom. Moreover, to use the system response for model extraction, two auxiliary signals are proposed to provide sufficient information for improving the accuracy of the dynamics of the system numerically. Additionally, two adaptive laws are proposed in each case to update the two introduced auxiliary signals. As a result, the controller controls the passive degrees of freedom after the active degrees of freedom converge to their desired trajectories. Simultaneously, the information collected from the system to update the auxiliary signals enhances the model accuracy. In the end, simulation results are presented to verify the performance of the proposed controller.

Direct adaptive compensation for actuator failures and dead-Zone constraints in tracking control of uncertain nonlinear systems

In this paper, a new tuning function backstepping control scheme is proposed for a class of parametric strict feedback nonlinear systems to accommodate actuator failures/faults and dead-zone constraints, where the failures/faults are uncertain in time, pattern, and values, and the dead-zone parameters are not available for feedback control design. Roughly speaking, such a scheme is developed in two steps below. First, by using an adaptive smooth inverse function to compensate for the dead-zone nonlinearity, we separate the coupling actuator dynamics into two parts, i.e., the dead-zone compensation errors and the nominal failure dynamics. Afterward, we further handle these two parts based on the techniques of robust adaptive approach and parametrization method. With our scheme, the global boundedness of the signals in the closed-loop system are ensured, and the tracking error is steered to zero asymptotically, regardless of the presence of uncertain failures/faults and dead-zone constraints. These results have also been verified through simulation studies.

An enhanced backstepping approach for motion control of underactuated autonomous surface vessels with input constraints

This paper focuses on the motion control of an underactuated autonomous surface vessel (UASV). A robust adaptive approach is proposed to control the UASV in spite of existence of uncertainties in the dynamic model and the external disturbances by integrating the backstepping method with input constraints, functional approximate technique (FAT) and projection operator. In the proposed controller, it is not required to compute the complex functions and their derivatives appearing in the traditional backstepping methods. The motion control strategy of the UASV is first derived based on the backstepping method. Then, the complex functions, appearing in the controller, are estimated by the FAT and Lyapunov based adaptive laws. This causes the inherent complexity of the backstepping technique, numerical derivatives of virtual inputs, is to be overcome. Moreover, the input limitations are considered by an auxiliary system. Stability analysis of system is performed by Lyapunov’s direct method. Several numerical simulations are carried out to confirm the effectiveness of the proposed method.

Second-order cone programming with probabilistic regularization for robust adaptive beamforming.

Probabilistic regularization (PR) is introduced to make superdirective array beamforming robust against sensor characteristic mismatches. The objective is to enlarge the directivity while ensuring robustness with high probability. The PR problem is solved via the second-order cone programming where the regularization parameter is chosen through a statistical analysis of the system perturbations, based on Monte Carlo simulations. Experiments are carried out on a miniaturized 3 × 3 uniform rectangular array without calibration. The results show that for this particular array, the PR method is robust to sensor mismatches and achieves a higher level of directivity compared with other robust adaptive beamforming approaches.