Conventional Adaptive Methods Research Articles

Sensorless control method of induction motors with new feedback gain matrix and speed adaptive law for low speed range

Abstract The conventional adaptive full-order observer-based speed sensorless control method for induction motor is prone to instability at low-speed region as improper feedback gain matrix and imprecise speed adaptive law are often used. To address these problems, a new feedback gain matrix design approach as well as a new speed adaptive law design technique are proposed in this paper. Firstly, considering that the d-axis current estimation error in the traditional feedback gain matrix design method has a great influence on the stability of the speed estimation algorithm, especially at low speeds, a new feedback gain matrix design method is proposed to minimize the d-axis current estimation error. Secondly, a new speed adaptive law design technique is studied based on the conventional method, which only requires the d-axis and q-axis current estimation error with a weight coefficient to be designed, simplifying the conventional speed adaptive law. Thirdly, the transfer function from the speed observation error to the proposed adaptive error is analyzed by Routh stability criterion theory, and the weight coefficient suitable for full range stable operation is determined by MATLAB software. Fourthly, the stability of the proposed method in this paper is analyzed using the poles distribution maps. Finally, the proposed method is experimentally verified based on a 2.2 kW induction motor experimental platform. The experimental results show that the proposed method can make the induction motor operate steadily at low-speed and zero-speed region with rated load. In addition, the proposed method has better anti-disturbance performance than the existing method.

A novel predefined performance barrier function based adaptive sliding mode control method for RLV under mismatched disturbance

PurposeThis paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.Design/methodology/approachA novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.FindingsIn contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.Originality/valueA novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

Seismic adaptive multiple subtraction using a structure-oriented matched filter

Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is used to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over-attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we develop a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. Our structure-oriented matched filter emphasizes the structures of predicted multiples, which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the effectiveness of our structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared with conventional methods on 2D regularly sampled data.

Incremental Variational Bayesian Gaussian Mixture Model With Decremental Optimization for Distribution Accommodation and Fine-Scale Adaptive Process Monitoring.

Due to the frequent changes in operating conditions, time-varying behaviors, including slow-varying dynamics and switching modes, commonly exist in industrial processes, resulting in different degrees of shifting in the process data distribution. When the data distribution shifts in a relatively wide range, conventional adaptive methods become ineffective since they are unable to distinguish normal shifts from real faults, leading to false alarms. In this study, an incremental variational Bayesian Gaussian mixture model (IncVBGMM) is proposed for developing a fine-scale adaptive monitoring scheme to efficiently accommodate the shifting data distribution caused by different degrees of time-varying behaviors. First, IncVBGMM with decremental optimization is proposed to adapt to the changing data distribution via the automatic complement of local models while reducing redundancy to optimize the mixture model. Then, a fine-scale adaptive monitoring scheme is built with physical interpretations to discern between normal shifts and real faults by joint analysis of the static and dynamic information. In addition, a novel monitoring statistic called the expectation of variational Bayesian inference distance (EVBID) is proposed, which can quantify the distance from samples to the variational monitoring model and indicate the fault effects. Case studies involving a real-world three-phase flow facility reveal that the proposed method can accurately differentiate various types of faults from normal shifts and effectively adapt to the time-varying dynamics.

Tuning functions based adaptive backstepping control for uncertain strict-feedback nonlinear systems using barrier Lyapunov functions with full state constraints

This paper investigates the tuning functions based adaptive backstepping control for a class of uncertain strict-feedback nonlinear systems using barrier Lyapunov functions with full state constraints. The proposed adaptive control scheme is constructed by combining the barrier Lyapunov functions (BLFs) with the tuning functions based adaptive backstepping control technique. Compared with the relevant results, the proposed adaptive control scheme is constructed to guarantee that the overparametrization problem inherent to the conventional adaptive backstepping control method is avoided and the full state constraints are not violated by introducing BLFs. Moreover, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded (UUB) and the tracking errors converge to a small neighborhood around zero by appropriately choosing design parameters. Simulation results of an electromechanical system are provided to verify the feasibility of the proposed adaptive control method. The simulation results clearly prove the effectiveness of our approach compared with the tuning functions based adaptive backstepping control (TFABC) and the traditional backstepping control design (BC) schemes that system state without constraints.

Nonlinear channel estimation based on robust distributed Hammerstein spline adaptive technique in wireless sensor network

In a wireless sensor network (WSN), channel estimation prior to data communication is an important task. The nonlinear behaviour of the channel makes the estimation procedure challenging. Further, the accuracy of the estimation algorithm deteriorates in the presence of impulsive noise. In such a scenario, the conventional adaptive filtering methods based on the mean square error cost function and its different variants are not suitable for estimating the channel. Hence, this paper proposes a robust Hammerstein spline adaptive filtering based on the inverse square root cost function to mitigate impulsive noise's effect and adapt to the nonlinearity. Further, to fasten the estimation procedure, the robust method is extended to the distributed WSN, where diffusion cooperation among the sensor node is incorporated to estimate the nonlinear channel. The theoretical steady state analysis of the proposed method is carried out and compared with the simulation results, which validates the algorithm performance. The simulation results confirm the robustness of the proposed method compared to other robust strategies based on maximum correntropy criteria, logarithmic hyperbolic cosine cost function, and Geman-McClure cost function.

Robust adaptive dynamic surface control scheme for a class of single‐input and single‐output uncertain nonlinear systems in strict‐feedback form

SummaryThis article presents a robust adaptive dynamic surface control using σ‐modification adaptation laws for a class of single‐input and single‐output (SISO) uncertain nonlinear systems in strict‐feedback form with parametric uncertainties and external disturbances. The proposed scheme is developed by employing dynamic surface control based robust adaptive control technique. The key features of the approach are that, first, the problem of explosion of complexity inherent in the conventional adaptive backstepping control method is avoided, second, the proposed approach of σ‐modification adaptation laws gives fast and accurate parameter estimation performance, and, third, the closed‐loop signals of the system are proven to be uniformly ultimately bounded (UUB) by using the Lyapunov stability theory. We show the effectiveness of our approach by simulating an electromechanical system.

Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method.

Waveguide Invariant-based mitigation of reverberation and interference in active and passive sonar

Multipath propagation poses many challenges for broadband sonar signal processing in underwater acoustic channels. The waveguide invariant (WI) provides a useful relation between the temporal and frequency fading characteristics in shallow-water channels that can been exploited to improve broadband target detection in both active and passive sonars. In active sonar, the WI motivates an alternative to conventional constant-false-alarm rate (CFAR) normalization of the background by averaging over observed time-frequency striations to estimate the reverberation level. A WI-based generalized likelihood ratio test is reviewed and shown to outperform conventional cell-averaged and frequency-invariant CFAR detectors in both simulation and with real Mediterranean data. In passive towed-array sonar, targets near endfire are often masked by angularly spread broadband multipath interference whose statistics must be estimated with limited training data for effective suppression. A WI relationship characterizing the frequency-dependence of wavenumber differences permits averaging of narrowband array snapshots to form a low-rank broadband “focused” covariance matrix. Simulation experiments in a realistic ocean environment are reviewed showing that adaptive beamforming using WI focused covariance matrices can provide a significant array gain improvement over conventional adaptive methods with limited observation time. Finally, the potential for waveguide invariant-based blind source separation is discussed. [Work supported by ONR.]

Adaptive subtraction using a convolutional neural network

Adaptive subtraction is a well-established routine technique in seismic data processing that relies on being able to match, in some sense, one dataset to another. It is a necessary part of many processing steps such as de-noising and de-multiple. The same matching methods are also used for advanced imaging steps such as non-stationary estimation of the Hessian as part of image-domain least-squares migration. Predictions of noise are often not as accurate as we would like because there are often shortcomings in the prediction method and because pragmatic acquisition compromises are made. The ubiquity of adaptive subtraction in the seismic processing workflow means there is always motivation to improve its performance. In this work, we propose a deep-learning approach for the adaptive subtraction of predicted multiple models from the input recorded data. It turns out that it is straightforward to use deep-learning networks to perform adaptive subtraction and as a result, benefit from the power of complex non-linear filters. Inspired by the idea of a convolutional autoencoder, we have developed a supervised convolutional autoencoder for adaptive subtraction. We demonstrate the proposed approach on synthetic and field data examples and compare the results using conventional adaptive subtraction methods to illustrate the effectiveness of our approach.

The Novel Singular-Perturbation-Based Adaptive Control with σ-Modification for Cable Driven System

Due to their large working space and fast response, cable driven systems have been widely applied in manufacturing, robotics and motion simulators, etc. However, the cable is flexible and tends to resonate at high frequencies, which raises challenges for the motion control of the cable driven system. To solve this problem, this paper proposes a singular-perturbation-based adaptive control method with σ-modification. Taking advantage of the multi-time scale characteristics, the flexible system is approximately decomposed into two subsystems, and then the damping compensation is designed in the boundary layer subsystem to enhance the tension stability. In addition, estimated parameters drift may occur for the reduced-order system. Thus, the σ-modification is proposed to ensure that the tracking and estimation errors converge to a bounded residual set. The Lyapunov stability theorem proves that the closed-loop system is stable and errors are ultimately uniformly bounded. A research prototype of a twin-motor cable driven system is developed, and experimental investigation is conducted on it. The experimental results show that the proposed control method can effectively suppress cable resonance at high frequencies. Compared with the conventional adaptive control method, it can significantly increase the system bandwidth.

Driver assistant yaw stability control via integration of AFS and DYC

This paper aims to develop an integrated adaptive control strategy to control vehicle lateral dynamics. A hierarchical control scheme consisted of two control levels is designed to keep the vehicle handling and improve yaw stability control using the coordination of active front steering (AFS) and direct yaw moment control (DYC). At the upper control level, the desired yaw moment and additive steering angle are obtained through proper adaptive laws in the first level adaptation (FLA) control structure. A constraint optimisation algorithm is utilised in the lower control level to adjust the impact of the steering and braking actuators. The desired yaw moment is converted into the brake torques and effectively distributed to the wheels. Improvement in the yaw stability control is demonstrated through simulation studies for a nonlinear 8 degree-of-freedom (DOF) vehicle model. Furthermore, the performance of the proposed control strategy is compared with that of the adaptive controller without integration. Simulation results of two different maneuvers point to the superiority of the proposed approach, highlighting some of its appealing features, like better tracking performance with lower control efforts in terms of brake torque and steering angle, as compared to the conventional adaptive method.

ON THE EVALUATION OF THE GRADIENT TREE BOOSTING MODEL FOR GROUNDWATER LEVEL FORECASTING

Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.

Integrating adaptive moving window and just-in-time learning paradigms for soft-sensor design

Most applications of soft sensors in process industries require learning from a stream of data, which may exhibit nonstationary dynamics, or concept drift. In this study, we develop a relevance vector machine (RVM) based novel adaptive learning algorithm called MWAdp-JITL, to meet the demands of continuous processes. The resulting algorithm combines active and passive learning: A moving window (MW) algorithm, which adapts the window size against virtual/real concept drifts, is coupled with a just-in-time learning (JITL) model, constructed using an appropriate region of historical data, and the ensemble weights of the MW and JITL models are adjusted for each query point. Tests on four real industrial datasets and a synthetic data, comprising various concept drift scenarios, show that MWAdp-JITL yields superior prediction accuracy and is generally more robust to changes in algorithm parameters compared to conventional adaptive learning methods and state-of-the-art algorithms from the literature. MWAdp-JITL complies with time limits of online prediction, and is applicable for high dimensional processes under various types of concept drifts. It is seen that MWAdp-JITL can successfully achieve a good balance in bias-variance tradeoff, justifying the use of only two exquisitely selected learners in ensemble learning.

Adaptive controls of FWER and FDR under block dependence

Often in multiple testing, the hypotheses appear in non-overlapping blocks with the associated p-values exhibiting dependence within but not between blocks. We consider adapting the Benjamini–Hochberg method for controlling the false discovery rate (FDR) and the Bonferroni method for controlling the familywise error rate (FWER) to such dependence structure without losing their ultimate controls over the FDR and FWER, respectively, in a non-asymptotic setting. We present variants of conventional adaptive Benjamini–Hochberg and Bonferroni methods with proofs of their respective controls over the FDR and FWER. Numerical evidence is presented to show that these new adaptive methods can capture the present dependence structure more effectively than the corresponding conventional adaptive methods. This paper offers a solution to the open problem of constructing adaptive FDR and FWER controlling methods under dependence in a non-asymptotic setting and providing real improvements over the corresponding non-adaptive ones.

Dual Adaptive Factors-Based Integrated Navigation Performance Improvement for Airborne POS

Airborne position and orientation system (POS) can provide high accuracy motion parameters for multiple aerial mission loads and has become a vital equipment in aerial remote sensing system. However, the airborne POS often suffers from the systematic kinematic model and observation model inaccuracy, which further affects the accuracy of motion parameters, and the conventional innovation-based adaptive method only deals with the observation model inaccuracy and results in the limited performance improvement. In this paper, a dual adaptive factors filtering algorithm is adopted to deal with the kinematic model and observation model inaccuracy together, which has been validated by the real flight experiment and the results show that the performance improvement has achieved.

Recursive Exponential Slow Feature Analysis for Fine-Scale Adaptive Processes Monitoring With Comprehensive Operation Status Identification

Due to the compensation of the control loops, industrial processes under feedback control generally reveal typical dynamic behaviors for different operation statuses. Conventional adaptive methods may update model falsely and thus result in invalid monitoring results, since they cannot effectively extract the feedback dynamic information and fail to accurately differentiate real anomalies from normal process changes. In this study, a recursive exponential slow feature analysis (ESFA) algorithm is developed for fine-scale adaptive monitoring to solve the problem of false model updating. First, an ESFA method is proposed to nonlinearly extract slow features, so that the general trend of the process variations can be better captured. On the basis of the ESFA model, a fine-scale adaptive monitoring scheme is developed to accurately capture the normal changes of industrial processes, including normal slow varying and normal shift of operation conditions. In this way, the normal slow varying can be effectively distinguished from incipient faults with unusual dynamic behaviors to avoid falsely adapting for the fault case, and the monitoring model can be correctly updated for new operation status after distinguishing real process anomalies from normal shifts of operation conditions. A simulation process and two real industrial processes are adopted to validate the performance of the proposed adaptive monitoring method. Experimental results show that the proposed method can effectively identify different operation statuses to decide whether to update the monitoring model or to raise an alarm.

A Robust Eigenanalysis Interference Canceler Using Sensitivity-Based Number of Sources Estimation

When the estimation of the number of sources is inaccurate and the expected signal is mixed in the training data, the conventional eigenanalysis interference canceler (EC) will result in the expected signal cancelation, which leads the performance of adaptive beamforming to be degraded significantly. In this paper, a robust eigenanalysis interference canceler (REC) algorithm is proposed to resolve this problem by using the minimum sensitivity-based number of sources estimation. In the proposed method, the number of sources is first pre-estimated by effectively using the minimum sensitivity function. Then, based on the eigenvalue decomposition of the array covariance matrix, the expected signal in the training data is determined and eliminated, so that the corresponding impact of the adaptive weight calculation can be avoided. Finally, the adaptive weight vector is obtained by orthogonally projecting the quiescent weight vector into the interference subspace. Simulation results show that the performance of the output signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm has improved significantly when compared with the conventional adaptive beamforming methods.

Robust adaptive beamforming method for active sonar in single snapshot

Forming narrow beams is a useful way for active sonar to anti-reverberation when it works in the shallow water. High-resolution adaptive beamforming with the performance of narrow beamwidths and low sidelobe levels is a better and more efficient method, particularly in the scenario where the installation space for sonar array is limited, such as hull-mounted sonar. Due to the short duration of target echo signal in the complex and varying acoustic channel, conventional adaptive beamforming methods are invalid. Therefore, this paper proposes a robust adaptive beamforming method for active sonar in single snapshot, also called the steered dominant mode rejection (STDMR). Firstly, STDMR steered the sample covariance matrix (STCM) based on wide-band focusing, which the needed number of snapshots is greatly reduced. Secondly, by partial eigendecomposition, the large eigenvalues of the STCM which are greater than the noise energy and their eigenvectors are used for dominant mode rejection (DMR). DMR is a typical eigenspace-based algorithm which has small computational load and fast convergence speed. Finally, modified with the methods of diagonal loading of 3-5dB over the noise energy and signal mismatch protection, improved the robustness of this method. Simulation and experimental data analysis shows that the STDMR method achieves narrow beams and low-level sidelobes in single snapshot. Hence, the STDMR beamformer is an appropriate implementation to use for active sonar detection systems.

Active Control of Longitudinal Vibration of a Time-Varying Shafting System With a Dynamic Interpolating Adaptive Method

An adaptive control method with dynamic interpolation is proposed for the active longitudinal vibration control of propulsion shafting systems. In such systems, the dynamics of longitudinal vibration change with the speed-dependent stiffness, which can result in a time-varying system as the shaft speed changes with time. A longitudinal vibration model is established for the investigation of the dynamic interpolating adaptive method (DIAM). In this model, the longitudinal vibration is induced by the disturbance exerted on the propeller (the left mass) and the control force is exerted on the thrust bearing (the right mass), which defines the disturbance channel and the control channel. The proposed DIAM is used to suppress longitudinal vibration transmission from the propeller to the thrust bearing by applying an active force on the right mass. The interpolation technique in DIAM updates the parameter-dependent compensator dynamically and eliminates the influence of parameter-dependent dynamics on the stability of control. Simulation results have demonstrated that the proposed DIAM is effective in suppressing longitudinal vibration of the thrust bearing in comparison to conventional adaptive methods.