Closed-loop Adaptive System Research Articles

Evaluation of the Display of Cognitive State Feedback to Drive Adaptive Task Sharing.

This paper presents an adaptive system intended to address workload imbalances between pilots in future flight decks. Team performance can be maximized when task demands are balanced within crew capabilities and resources. Good communication skills enable teams to adapt to changes in workload, and include the balancing of workload between team members This work addresses human factors priorities in the aviation domain with the goal to develop concepts that balance operator workload, support future operator roles and responsibilities, and support new task requirements, while allowing operators to focus on the most safety critical tasks. A traditional closed-loop adaptive system includes the decision logic to turn automated adaptations on and off. This work takes a novel approach of replacing the decision logic, normally performed by the automation, with human decisions. The Crew Workload Manager (CWLM) was developed to objectively display the workload between pilots and recommend task sharing; it is then the pilots who “close the loop” by deciding how to best mitigate unbalanced workload. The workload was manipulated by the Shared Aviation Task Battery (SAT-B), which was developed to provide opportunities for pilots to mitigate imbalances in workload between crew members. Participants were put in situations of high and low workload (i.e., workload was manipulated as opposed to being measured), the workload was then displayed to pilots, and pilots were allowed to decide how to mitigate the situation. An evaluation was performed that utilized the SAT-B to manipulate workload and create workload imbalances. Overall, the CWLM reduced the time spent in unbalanced workload and improved the crew coordination in task sharing while not negatively impacting concurrent task performance. Balancing workload has the potential to improve crew resource management and task performance over time, and reduce errors and fatigue. Paired with a real-time workload measurement system, the CWLM could help teams manage their own task load distribution.

Adaptive Finite-Time Controller Design for T-S Fuzzy Systems.

This paper studies the adaptive finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel adaptive state feedback control scheme for the T-S fuzzy systems is proposed, and the scheme is developed based on finite-time Lyapunov theorem and adaptive backstepping-like method. Augmented dynamics are introduced in the design of finite-time stabilization controllers to construct suitable finite-time Lyapunov functions. It is shown that finite-time convergence of the closed-loop adaptive control system can be achieved, and the potential controller singularity problem caused by the augmented dynamics can be avoided. In addition, constructive procedures to obtain such an adaptive finite-time controller are given. Convergence time as a transient performance specification is also taken into account, and a finite upper bound on the convergence time is estimated. Finally, two numerical examples are provided to illustrate the effectiveness and practicality of the proposed adaptive control approach.

Adaptive finite‐time control for a class of uncertain high‐order non‐linear systems based on fuzzy approximation

The problem of adaptive practical finite-time control is considered for a class of single-input and single-output non-linear systems, in which the system non-linear functions are assumed to be unknown. By combining adaptive fuzzy control approach with the backstepping technology, a backstepping-based adaptive fuzzy finite-time control scheme is proposed. In the control design procedure, fuzzy logic systems are employed to identify the non-linear uncertainties. The stability analysis of the adaptive closed-loop systems is proposed based on the finite-time Lyapunov stability theory. The proposed adaptive fuzzy controller guarantees that all the closed signals are semi-global practical finite-time stability while the tracking error converges to a small neighbourhood of the origin. Finally, simulation results are presented to validate the effectiveness of our results.

Training to improve contrast sensitivity in amblyopia: correction of high-order aberrations

Perceptual learning is considered a potential treatment for amblyopia even in adult patients who have progressed beyond the critical period of visual development because adult amblyopes retain sufficient visual plasticity. When perceptual learning is performed with the correction of high-order aberrations (HOAs), a greater degree of neural plasticity is present in normal adults and those with highly aberrated keratoconic eyes. Because amblyopic eyes show more severe HOAs than normal eyes, it is interesting to study the effects of HOA-corrected visual perceptual learning in amblyopia. In the present study, we trained twenty-six older child and adult anisometropic amblyopes while their HOAs were corrected using a real-time closed-loop adaptive optics perceptual learning system (AOPL). We found that adaptive optics (AO) correction improved the modulation transfer functions (MTFs) and contrast sensitivity functions (CSFs) of older children and adults with anisometropic amblyopia. When perceptual learning was performed with AO correction of the ocular HOAs, the improvements in visual function were not only demonstrated in the condition with AO correction but were also maintained in the condition without AO correction. Additionally, the learning effect with AO correction was transferred to the untrained visual acuity and fellow eyes in the condition without AO correction.

${\mathcal L}_{1}$ Adaptive Control for a Class of Uncertain Nonaffine-in-Control Nonlinear Systems

This technical note presents an ${\mathcal L}_{1}$ adaptive controller for a class of nonlinear uncertain systems, nonaffine in control. As compared to previous ${\mathcal L}_{1}$ architectures for nonlinear nonaffine-in-control systems, the solution discussed in this technical note admits a broader class of nonlinear systems by relaxing some of the (restrictive) assumptions previously made. We also consider a more general filtering structure and prove the results using partial knowledge of the control input actuator model. With the presented control architecture we are able to improve the performance and robustness of the closed-loop adaptive system, compared to prior results of ${\mathcal L}_{1}$ adaptive control for nonaffine-in-control systems.

A Heuristic Self-Adaptive Medium Access Control for Resource-Constrained WBAN Systems

This paper presents a heuristic-based approach to self-adapt and reconfigures the wake-up schedule of the nodes in wireless body area networks (WBANs). A latency-energy-optimized traffic-aware dynamic medium access control protocol is presented. The protocol is based on an adaptive algorithm that allows the sensor nodes to adapt their wake-up and sleep patterns efficiently in static and dynamic traffic variations. The heuristic approach helps to characterize the algorithmic parameters with an objective to investigate the behavior of the convergence patterns of the WBAN nodes in a non-linear system. An open-loop form is developed by keeping the wake-up interval (I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wu</sub> ) fixed followed by the closed-loop adaptive system which updates I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wu</sub> on every wake-up instant. An exhaustive search is conducted for different initial wake-up interval values which show that (on average) the algorithm parameters behave monotonically in open-loop systems, whereas a decaying function in the closed-loop form. Various performance metrics, such as energy consumption, packet delay, packet delivery ratio, and convergence speed (for reaching a steady state), are evaluated. It is observed that the convergence time varies from 8 to 72 s under fixed packet transmission rate, whereas the algorithm re-converges (within 8 s) whenever the transmission rate changes.

Observer-Based Adaptive Fuzzy Control for a Class of Nonlinear Delayed Systems

This paper considers the problem of observer-based adaptive fuzzy control for a class of nonlinear time-delay systems in nonstrict-feedback form, which includes the nonlinear strict-feedback systems as a special case. An adaptive fuzzy output feedback backstepping approach is first proposed for nonlinear systems in nonstrict-feedback form. Fuzzy logic systems are used to approximate the unknown nonlinear functions. Adaptive technique and backstepping are utilized to construct a controller. The proposed adaptive fuzzy output feedback controller guarantees that all the signals in the adaptive closed-loop system are semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the presented approach.

Adaptive control of second-order plants in the presence of unmodeled dynamics

In this paper, we provide a sufficient condition for global boundedness of the closed-loop adaptive system comprised of a general second-order plant, whose state variables are accessible, in the presence of a minimally restrictive class of unmodeled dynamics.

A Step towards EEG-based brain computer interface for autism intervention.

Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention.

Separation force analysis and prediction based on cohesive element model for constrained-surface Stereolithography processes

Constrained-surface based Additive Manufacturing (AM) processes have been widely used in both academia and industry for the past few years. Despite the advantages of constrained-surface based AM processes, it has not been widely used in practice. A main reason for this is that a substantial separation force is required to separate the cured part from the material vat during the pulling-up stage, which may damage the cured part and reduce the reliability of the process. The solutions proposed previously to reduce this separation force recommend using an intermediate coating material (e.g., Teflon and silicone films) between the cured part and the vat. This, however, has only negligible effects in reducing the separation force. In this work, the pulling-up process is modeled within the framework of mechanics-based principles. In particular, the cohesive zone model (CZM) is adopted to characterize the separation mechanism, and finite element (FE) simulation is carried out to investigate the separation process using the commercially available FE software, Abaqus. A new simple optimization scheme is also proposed to estimate the constitutive cohesive stiffness parameters from experimental measurements. These constitutive parameters are very difficult to estimate using the standard mechanical tests. The proposed work based on sound mechanics-based principles can be used for reliable prediction of pulling-up speed, and thus, is likely to be useful in devising an adaptive closed-loop system to control the pulling-up process and achieve a reliable AM approach.

Base Algorithms of the Direct Adaptive Position-Path Control for Mobile Objects Positioning

Problem of a mobile object positioning in the presence of determinate disturbances is considered in this paper. A mobile object is described by kinematics and dynamics equations of a solid body in three dimensional space. The control inputs of the mobile object are forces and torques. Design of adaptive control is based on position-path control method for mobile objects. In this article two algorithms of the adaptive position-path control are developed. The first algorithm is adaptive position-path control with integration component and a reference model. The second algorithm is adaptive position-path control with a reference model and an extended mobile robot model. Block diagram of the direct adaptive position-path control system with a reference model is suggested. Design procedures of the adaptive position-path control systems and stability analysis of the closed-loop systems are presented. Computer simulation results of the designed adaptive closed-loop systems with both constant and variable disturbances are presented. On base of the analysis and modeling results conclusions are provided.

A Nonsingular Terminal Sliding Mode Approach Using Adaptive Disturbance Observer for Finite-Time Trajectory Tracking of MEMS Triaxial Vibratory Gyroscope

This paper develops a nonsingular terminal sliding mode controller (NTSMC) with adaptive disturbance observer (ADOB) for finite-time trajectory tracking of a MEMS triaxial vibratory gyroscope, which has parameter variations and external high-amplitude disturbance. A novel sliding mode controller with adaptive disturbance observer is designed to reconfigure the parameter variations and external high-amplitude disturbance and reduce the chattering phenomenon on the sliding surface greatly through setting the switching gain in control signal as a smaller value. The disturbance adaptive law is derived to guarantee the stability of the closed-loop adaptive control system via the Lyapunov criterion. The simulation results are performed to verify the effectiveness of the presented schemes.

Study of Automobile Suspension System Vibration Characteristics Based on the Adaptive Control Method

An automotive suspension determines both the driving stability and comfort of the vehicle occupants. This paper establishes two kinds of two degrees of freedom for the automobile suspension vibration model and uses the PID controller to establish an automobile suspension adaptive open-loop and closed-loop control system. Respectively, by step interference, white noise and sinusoidal interference for the input, studying the vibration characteristics of the vibration model in the vertical direction. By numerical simulation, we obtain the suspension of the vertical displacement and acceleration-time graphs. The simulation results show that the vibration characteristics of the first model are more in accordance with the actual situation of the car, and the closed-loop control is better than the open-loop control. The adaptive closed-loop control system can reduce the output displacement of automobile suspension to around 1% of the interference road input displacement. The output acceleration value is small, and the acceleration changes smoothly. The results verify the rationality and validity of the automobile suspension model and adaptive control system, which provides a theoretical foundation for the design and optimization of the automobile suspension system.

Backstepping-based adaptive control for underactuated systems

A backstepping-based adaptive control scheme is presented for uncertain underactuated systems. On the basis of backstepping approach, virtual control components are first constructed and final control term is then derived by defining an integrated error surface. Proposed control scheme utilises wavelet neural network for the approximation of system uncertainties and thus relaxes the constraint of fully known system dynamics. Update laws for wavelet network parameters are derived in Lyapunov sense. Proposed control scheme guarantees the uniform ultimate boundedness of the closed loop adaptive system. Simulation included illustrates the effectiveness of the proposed approach.

Yaw Moment Compensation for Humanoid Robot via Arms Swinging

This paper investigates the problems of the z-axis dynamic balance and high energy consumption in practical application of humanoid robot. Considering z-axis dynamic balance conditions, a novel yaw moment compensation solu- tion based on arms-swinging is proposed by computing the optimal arms-swinging trajectories to counteract the yaw mo- ment caused by the movement of humanoid robot. Given the optimized arms-swinging motion, an adaptive control system is proposed to track the desired trajectories with model uncertainties. From the Lyapunov stability analysis, the adaptation law is induced and the uniformly boundness of all signals in a closed-loop adaptive system is proved. Simulation results validate the proposed method.

Transient performance improvement in model reference adaptive control using [formula omitted] optimal method

Poor transient performance is considered as a drawback of model reference adaptive control. To achieve a better transient behavior, the standard model reference adaptive controller is modified by adding a compensator in this paper. Based on the augmented tracking error dynamics, the compensator can be designed as an H∞ optimal controller to eliminate the adverse effect caused by the parameter estimation error in initial stage, so that the transient performance is improved. The proposed modified adaptive controller will not destroy the asymptotic stability of the closed-loop adaptive system as well as the ideal properties of adaptive law, and a predefined transient performance index is taken into account throughout the controller design. The performance analysis using L∞ tracking error bound and mean squared tracking error bound in any interval reveals that the amount of the transient performance improvement depends on the performance level of the H∞ compensator. Simulation results of the designed control scheme application to a flight control system are presented to show the desired transient.

Adaptive Tuning Functions Tracking Control with Nonlinear Adaptive Observers

Abstract The paper is dedicated to the derivation of a unified approach for nonlinear adaptive closed loop system design with nonlinear adaptive state and parameter observers combined with tuning functions-based nonlinear adaptive control for trajectory tracking. The proposed approach guarantees asymptotic stability of the closed loop nonlinear adaptive system with respect to the tracking and state estimation errors and Lyapunov stability of the parameter estimator. The advantages of the approach are the lack of over-parametrization, resulting in a minimal number of estimator equations and the preservation of the overdamped performance specifications of the closed loop nonlinear adaptive system in its whole range of operation. The application of the approach to a permanent magnet synchronous motor driven inverted pendulum concludes with simulation of the closed loop nonlinear adaptive system time responses.

State derivative feedback for adaptive cancellation of unmatched disturbances in unknown strict-feedback LTI systems

Solutions exist for the problem of canceling sinusoidal disturbances by the measurement of the state or by the measurement of an output for linear and nonlinear systems. In this paper, an adaptive backstepping controller is designed to cancel sinusoidal disturbances forcing an unknown linear time-invariant system in controllable canonical form which is augmented by a linear input subsystem with unknown system parameters. The state-derivatives of the original subsystem and the state of the input subsystem are the only measurements that are used in the design of the controller. The design is based on four steps, (1) parametrization of the sinusoidal disturbance as the output of a known feedback system with an unknown output vector that depends on unknown disturbance parameters, (2) design of an adaptive disturbance observer for both disturbance and its derivative, (3) design of an adaptive controller for the virtual control input, and (4) design of the final adaptive controller by using the backstepping procedure. It is proven that the equilibrium of the closed-loop adaptive system is stable and the state of the considered original subsystem converges to zero as t→∞ with perfect disturbance estimation. The effectiveness of the controller is illustrated with a simulation example of a third order system.

Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.

Robust adaptive fault‐tolerant control for linear systems with actuator failures and mismatched parameter uncertainties

In this study, a direct adaptive control scheme is developed to solve the robust fault-tolerant control problem for linear systems with mismatched parameter uncertainties, disturbances and actuator faults including loss of effectiveness, outage and stuck. Although the unparameterisable time-varying stuck faults and the upper bounds of disturbances are unknown, the adaptive laws are proposed to estimate the unknown controller parameters online. Then, by using the information from the adaptive mechanism, a class of robust adaptive state feedback controllers are constructed for automatically compensating the actuator faults, external disturbances and mismatched parameter uncertainties. Moreover, on the basis of Lyapunov stability theory, it is proved that the resulting adaptive closed-loop system can be guaranteed to be asymptotically stable in the presence of actuator faults, disturbances and mismatched parameter uncertainties. Finally, simulation results are presented to illustrate the efficiency of the proposed fault-tolerant design approach.