Simple Adaptive Scheme Research Articles

Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances

Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters perturbation) coupling disturbances weaken the motion performance of robotic systems, an observer-based finite-time prescribed performance sliding mode control with faster reaching law is proposed for robotic manipulators equipped with dual-motor joints (DMJs). In the case where the backlash information is completely unknown, the backlash is maximally eliminated using a simple but efficient dual-motor adaptive anti-backlash strategy. Thus, the design of position tracking controllers for DMJs can be simplified. Then, to deal with the influence of disturbances and residual uncertainties (excluding backlash), a novel finite-time adaptive sliding mode disturbance observer (ASMDO) is proposed to practically estimate the lumped uncertainties where their upper bounds are assumed to be unknown. Finally, a finite-time composite fast non-singular terminal sliding mode (TSM) controller, integrated with the prescribed performance principle, is proposed in this paper. To enhance the convergence rate, a novel TSM-type reaching law has been developed. The controller ensures that the tracking error is not only stabilized within a finite-time convergence rate but also adheres to a predefined maximum transient-steady-state error. The proposed scheme is implemented through simulation and experimental results, demonstrating its superior performance.

Preference-based multi-objective evolutionary algorithm with linear combination scalarizing function and reference point adjustment

In practice, the decision-maker (DM) may be only interested in a particular part of Pareto optimal front (PF). For this reason, many preference-based multi-objective evolutionary algorithms (MOEA) have been proposed to find a solution set that approximates the region of the interest (ROI). Most existing preference-based methods focus on selecting solutions in the ROI. Nevertheless, the enhancement of convergence in preference-based MOEAs has been neglected. Most decomposition-based approaches often employ the achievement scalarizing function (ASF) as their scalarizing function. However, it holds a weaker search ability than the weighted sum function (WSF) despite its capability to tackle problems with arbitrary PF geometries. In order to strengthen the selective pressure toward the PF, this paper proposes a new scalarizing function, LSF, which is a linear combination of the WSF and the ASF. Then, a simple adaptive penalty scheme is employed in LSF to balance the search ability and robustness. To focus the search on the ROI, we develop a reference point adjustment method that dynamically adjusts the position of the reference point according to the distance from the approximated target point. We apply the above two innovations to the MOEA/D framework and propose a new preference-based MOEA, namely RAMOEAD. Experimental results show that RAMOEAD is highly competitive when compared with five state-of-the-art preference-based MOEAs. Finally, the proposed algorithm is extended to double reference points for solving the problems in which the ROIs are defined by the reservation and aspiration points.

Neural Network-Based Knowledge Transfer for Multitask Optimization.

Knowledge transfer (KT) is crucial for optimizing tasks in evolutionary multitask optimization (EMTO). However, most existing KT methods can only achieve superficial KT but lack the ability to deeply mine the similarities or relationships among different tasks. This limitation may result in negative transfer, thereby degrading the KT performance. As the KT efficiency strongly depends on the similarities of tasks, this article proposes a neural network (NN)-based KT (NNKT) method to analyze the similarities of tasks and obtain the transfer models for information prediction between different tasks for high-quality KT. First, NNKT collects and pairs the solutions of multiple tasks and trains the NNs to obtain the transfer models between tasks. Second, the obtained NNs transfer knowledge by predicting new promising solutions. Meanwhile, a simple adaptive strategy is developed to find the suitable population size to satisfy various search requirements during the evolution process. Comparison of the experimental results between the proposed NN-based multitask optimization (NNMTO) algorithm and some state-of-the-art multitask algorithms on the IEEE Congress on Evolutionary Computation (IEEE CEC) 2017 and IEEE CEC2022 benchmarks demonstrate the efficiency and effectiveness of the NNMTO. Moreover, NNKT can be seamlessly applied to other EMTO algorithms to further enhance their performances. Finally, the NNMTO is applied to a real-world multitask rover navigation application problem to further demonstrate its applicability.

A framework based on generational and environmental response strategies for dynamic multi-objective optimization

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

Hybrid Adaptive Control for Tiltrotor Aircraft Flight Control Law Reconfiguration

Tiltrotor aircrafts have both fixed-wing control surfaces and helicopter rotors for attitude control. The redundancy of control surfaces provides the possibility for the control system to reconfigure the control law when actuator faults occur during flight. Possible actuator faults have been classified into two categories: predictable and unpredictable faults, and a different strategy has been adopted to deal with each kind of fault. Firstly, the predictable faults are handled by a multiple-model switching adaptive scheme. These kinds of faults are modeled, and their corresponding controllers are derived offline. Secondly, since the degree of drop in aerodynamic effectiveness cannot be predicted a priori, unpredictable faults are handled by a simple adaptive control scheme, to force the plant with faults to track the prescribed reference model. The presented methodology has been verified by nonlinear full-envelope flight simulation for both categories of actuator faults. The predictable fault is represented by the elevator floating. Elevator damage causing an aerodynamic effectiveness drop by 80% is chosen as the example of unpredictable fault. Both faults are simulated at the late stage of the tiltrotor conversion mode. Results show that the presented strategy of reconfiguration is able to detect the fault rapidly and stabilize the aircraft when a fault occurs, while the aircraft motion diverges without the reconfiguration scheme. The aircraft also presents a relatively good performance under controller reconfiguration with a well-tracked conversion path.

Adaptive robust output tracking of uncertain strict-feedback nonlinear systems with full state constraints via control schemes with simple structure

In this paper, the problem of output tracking control is considered for a class of uncertain strict-feedback nonlinear systems with virtual control coefficients and full state constraints. It is assumed only that the nonlinear uncertainties of the systems are some smooth functions enough to guarantee the existence of solutions to the differential equations which describe uncertain systems, and that the virtual control coefficients are unknown. Based on backstepping algorithm, by combining the barrier Lyapunov function with an integral inequality reported in the current control literature, a design method is presented whereby a simple adaptive robust output tracking control scheme can be synthesised. The presented design method can evade the repeated differentiation problem appearing in using backstepping algorithm, and need not know all the nonlinear upper bounds of uncertainties, which are repeatedly employed at each step of backstepping procedure. It is also shown that the output tracking error of the uncertain nonlinear systems with both unknown virtual control coefficients and full state constraints can converge uniformly exponentially towards a ball which can be as small as desired. Finally, a numerical example is given, and the corresponding simulations are also implemented to demonstrate the simplicity of the proposed method and the validity of the theoretical results.

Synchronization of stochastic fractional-order model of muscular blood vessels

This study deals with the synchronization problem of the stochastic fractional-order biomathematical model of muscular blood vessels (SFOMBVs) with nonlinear inputs using a state-feedback adaptive control law, in which the effects of uncertainties and external disturbances are considered in the modeling. The linear state feedback and adaptive laws are combined via an adaptive update law, leading to a simple and robust adaptive feedback scheme. The synchronization and tracking performances of the SFOMBV master and slave systems are investigated, and the stability of the proposed control system is proved based on stochastic analysis techniques and Lyapunov theory. The simulation results show that the proposed control method can lead an abnormal muscular blood vessel to a normal trajectory with good robustness by reducing the tracking error.

Divorce rate in monogamous birds increases with male promiscuity and migration distance.

Socially monogamous birds may break up their partnership by a so-called 'divorce' behaviour. Divorce rates vary immensely across avian taxa that have a predominantly monogamous social mating system. Although various factors associated with divorce have been tested, broad-scale drivers of divorce rate remain contentious. Moreover, the influence of sexual roles in divorce still needs further investigation because of the conflicting interests of males and females over mating and fertilization. Here, we applied phylogenetic comparative methods to analyse one of the largest datasets ever compiled that included divorce rates from published studies of 186 avian species from 25 orders and 61 families. We tested correlations between divorce rate and a group of factors: 'promiscuity' of both sexes (propensity to polygamy), migration distance and adult mortality. Our results showed that only male promiscuity, but not female promiscuity, had a positive relationship with divorce rate. Furthermore, migration distance was positively correlated with divorce rate, whereas adult mortality rate showed no direct relationship with divorce rate. These findings indicated that divorce might not be a simple adaptive (by sexual selection) or non-adaptive strategy (by accidental loss of a partner) in birds but it could be a mixed response to sexual conflict and stress from the ambient environment.

Uncertainty Estimation of Connected Vehicle Penetration Rate

Knowledge of the connected vehicle (CV) penetration rate is crucial for realizing numerous beneficial applications during the prolonged transition period to full CV deployment. A recent study described a novel single-source data penetration rate estimator (SSDPRE) for estimating the CV penetration rate solely from CV data. However, despite the unbiasedness of the SSDPRE, it is only a point estimator. Consequently, given the typically nonlinear nature of transportation systems, model estimations or system optimizations conducted with the SSDPRE without considering its variability can generate biased models or suboptimal solutions. Thus, this study proposes a probabilistic penetration rate model for estimating the variability of the results generated by the SSDPRE. An essential input for this model is the constrained queue length distribution, which is the distribution of the number of stopping vehicles in a signal cycle. An exact probabilistic dissipation time model and a simplified constant dissipation time model are developed for estimating this distribution. In addition, to improve the estimation accuracy in real-world situations, the braking and start-up motions of vehicles are considered by constructing a constant time loss model for use in calibrating the dissipation time models. VISSIM simulation demonstrates that the calibrated models accurately describe constrained queue length distributions and estimate the variability of the results generated by the SSDPRE. Furthermore, applications of the calibrated models to the next-generation simulation data set and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations. Funding: This work was supported by The University of Hong Kong [Francis S Y Bong Professorship in Engineering and Postgraduate Scholarship] and by the Council of the Hong Kong Special Administrative Region, China [Grants 17204919 and 17205822]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.1209 .

Improving Face Recognition by Integrating Decision Forest into GAN

ABSTRACT Posture variation and self-occlusion are well-known factors that can compromise the accuracy and robustness of face recognition systems. There are a variety of ways to combat the challenges listed above, include using Generative Adversarial Networks (GANs). Nevertheless, many GAN methods cannot guarantee high-quality frontal-face images, which can improve recognition accuracy and verification when applied to multiple datasets. Recent results have proven that the two-pathway GAN (TP-GAN) method is superior to many traditional GAN deep learning methods that provide better face-texture details due to a unique architecture that enables the method to perceive global structure and local details in a supervised fashion. Although the TP-GAN overcomes some of the difficulties associated with generating photorealistic frontal views through the use of texture information provided by landmark detection and synthesis functions, it is difficult to replicate across different datasets. Particularly, under extreme pose scenarios, TP-GAN fails to further boost photo-realistic face frontalization image samples, minimizes the training time, and reduces computational resources, all of which result in substantially lower performance. This paper proposes simple adaptive strategies for overcoming TP-GAN’s inherent limitations. First, we incorporate the powerful discrimination capabilities of a decision forest into the discriminator of a TP-GAN. This method will result in a more stable discriminator model over time. Secondly, we acclimate a data augmentation technique along with a method which reduces training errors and accelerates the convergence of existing learning algorithms. Our proposed approaches are evaluated on three datasets, Multi-PIE, FEI and CAS-PEAL. We demonstrate both quantitatively and qualitatively that our proposed approaches can enhance TP-GAN performance by restoring identity information contaminated by variations in posture and self-occlusion, resulting in high quality visualizations and rank-1 individual face identification.

On adaptive attitude tracking control of spacecraft: A reinforcement learning based gain tuning way with guaranteed performance

This paper investigates the attitude tracking control problem of a rigid spacecraft subject to inertial uncertainties, uncertain space perturbations and actuator saturation. Firstly, a static inertial-matrix free attitude controller is designed to guarantee the appointed-time convergence and tracking accuracy indicators of the spacecraft. Then, two-layer critic-action NNs are used to tune and optimize the control gains adaptively to improve the robustness and tracking performance of the devised static attitude controller via exploring the reinforcement learning (RL) technique. Compared with the existing attitude control methods, the prominent advantage of our work is that the devised controller is inertial-matrix free with a simple RL-based adaptive parameter tuning scheme, which reduces the conservativeness of the traditional attitude controllers with fixed control gains and is also easy to be achieved. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed attitude control method.

Human-in-the-loop handling of knowledge drift

We introduce and study knowledge drift (KD), a special form of concept drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, the introduction of a new is-a relation between two concepts might be confused with changes to those individual concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in human-in-the-loop applications like smart personal assistants the user knows what kind of drift occurred recently, if any. Motivated by this observation, we introduce trckd, a novel approach that combines two automated stages—drift detection and adaptation—with a new interactive disambiguation stage in which the user is asked to refine the machine’s understanding of recently detected KD. In addition, trckd implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that, when the structure of the concept hierarchy drifts, a handful of queries to the user are often enough to substantially improve prediction performance on both synthetic and realistic data.

An Efficient Threshold Dynamics Method for Topology Optimization for Fluids

We propose an efficient threshold dynamics method for topology optimization for fluids modeled with the Stokes equation. The proposed algorithm is based on minimization of an objective energy function that consists of the dissipation power in the fluid and the perimeter approximated by nonlocal energy, subject to a fluid volume constraint and the incompressibility condition. We show that the minimization problem can be solved with an iterative scheme in which the Stokes equation is approximated by a Brinkman equation. The indicator functions of the fluid-solid regions are then updated according to simple convolutions followed by a thresholding step. We demonstrate mathematically that the iterative algorithm has the total energy decaying property. The proposed algorithm is simple and easy to implement. A simple adaptive time strategy is also used to accelerate the convergence of the iteration. Extensive numerical experiments in both two and three dimensions show that the proposed iteration algorithm converges in much fewer iterations and is more efficient than many existing methods. In addition, the numerical results show that the algorithm is very robust and insensitive to the initial guess and the parameters in the model.

Ensemble of resource allocation strategies in decision and objective spaces for multiobjective optimization

A well exploitation of computational resource is essential when solving high-dimensional multiobjective problems (MOPs). Since many recent strategies presented for decision space and objective space are in consideration of searching efficiency, the collaboration between the two spaces is a promising approach for high-dimensional optimization. In this article, resource allocation strategies for both decision and objective spaces are readjusted and cooperated for complex MOPs. A metric-based variable partition strategy is introduced and a simple reference adaptation strategy is adopted to specify the searching orientations in the decision space and the objective space respectively. Subsequently, based on the above-mentioned basic techniques, three different evolutionary strategies are further designed to strengthen the directional convergence and preserve the diversity of target regions in a collaborative fashion. Several benchmark instances and a practical optimization problem are adopted in the experimental study. The effectiveness and rationality of the proposed resource allocation approach have been demonstrated by the experimental results.

Fundamental Limits in Bayesian Thermometry and Attainability via Adaptive Strategies.

We investigate the limits of thermometry using quantum probes at thermal equilibrium within the Bayesian approach. We consider the possibility of engineering interactions between the probes in order to enhance their sensitivity, as well as feedback during the measurement process, i.e., adaptive protocols. On the one hand, we obtain an ultimate bound on thermometry precision in the Bayesian setting, valid for arbitrary interactions and measurement schemes, which lower bounds the error with a quadratic (Heisenberg-like) scaling with the number of probes. We develop a simple adaptive strategy that can saturate this limit. On the other hand, we derive a no-go theorem for nonadaptive protocols that does not allow for better than linear (shot-noise-like) scaling even if one has unlimited control over the probes, namely, access to arbitrary many-body interactions.

Sequential adaptive design for jump regression estimation

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this article presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was to accelerate imaging speed in high-resolution material imaging, and the second was to use sequential design for mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, and thus many existing design optimization approaches cannot be used, as they assume a continuous regression function. Although some existing adaptive design strategies developed from the treed regression models can handle the discontinuities, the related Bayesian model estimation approaches come with computationally expensive Markov Chain Monte Carlo algorithms for posterior inferences and the subsequent design point selections, which may not be applicable for the first motivating example that requires the computation to be faster than the original imaging speed. In addition, the treed models are based on domain partitioning and are inefficient in cases when the discontinuities occur at complex sub-domain boundaries. In this article, we propose a simple and effective adaptive design strategy for regression analysis with discontinuities. After some statistical properties of the estimated regression model are derived in cases with a fixed design, a new criterion for sequentially selecting the design points is suggested. The suggested sequential design selection procedure is then evaluated using a comprehensive simulation study and demonstrated using two motivating examples.

Depth-Adaptive Object Identification Using Terahertz Time-Domain Spectroscopic Data

This study focused on machine-learning-based object recognition using the terahertz (THz) band time-domain spectroscopy (TDS) measurements. The reflection responses from each object showed severe depth dependence due to the out-of-focus effect, which was hardly compensated in the analytical propagation model. Such depth dependence severely limits the application range of THz-TDS-based object recognition, where the depth adjustment is not easily achieved, such as in subsurface imaging or industrial product inspection in an assembly line. In this study, we developed a simple adaptive depth signal conversion scheme using a calibration object before neural-network-based supervised learning. Experimental validations using several mono- or disaccharides and their THz-TDS data demonstrated that our compensation scheme successfully enhanced the recognition ratio.

A posteriori error estimates in W1,p × Lp spaces for the Stokes system with Dirac measures

We design and analyze a posteriori error estimators for the Stokes system with singular sources in suitable W1,p×Lp spaces. We consider classical low-order inf-sup stable and stabilized finite element discretizations. We prove, in two and three dimensional Lipschitz, but not necessarily convex polytopal domains, that the devised error estimators are reliable and locally efficient. On the basis of the devised error estimators, we design a simple adaptive strategy that yields optimal experimental rates of convergence for the numerical examples that we perform.

Uniform error bounds for fast calculation of approximate Voigt profiles

This paper presents uniform error bounds for fast calculation of approximate Voigt profiles that can be useful with the computationally demanding broadband line-by-line analysis of radiative transfer in the atmosphere. Formal proofs are given and rigorous criteria are provided to determine the domain on which the Voigt profile can be approximated by the Lorentz profile within any required accuracy. The most accurate Voigt-implementation to date can then be used to determine the required threshold parameters. Since most of the broadband radiative transfer calculations in the atmosphere will pertain to far wing computations, the potential saving in time is almost the same as by replacing the Voigt computation for the Lorentzian altogether completely. The error bounds can furthermore be used to derive a simple and efficient subband adaptive line selection strategy which can be used to rigorously exclude lines that will contribute to the resulting absorption coefficient less than any given threshold.

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.

The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread of memes through social networks. The usefulness of these stochastic process models within a host of economic sectors and scientific disciplines is undercut by the processes' computational burden: complexity of likelihood evaluations grows quadratically in the number of observations for both the temporal and spatiotemporal Hawkes processes. We show that, with care, one may parallelize these calculations using both central and graphics processing unit implementations to achieve over 100-fold speedups over single-core processing. Using a simple adaptive Metropolis-Hastings scheme, we apply our high-performance computing framework to a Bayesian analysis of big gunshot data generated in Washington D.C. between the years of 2006 and 2019, thereby extending a past analysis of the same data from under 10,000 to over 85,000 observations. To encourage widespread use, we provide hpHawkes, an open-source R package, and discuss high-level implementation and program design for leveraging aspects of computational hardware that become necessary in a big data setting.