Promoting contextual vocabulary learning through an adaptive computer‐assisted EFL reading system

The study developed an adaptive computer‐assisted reading system and investigated its effect on promoting English as a foreign language learner‐readers' contextual vocabulary learning performance. Seventy Taiwanese college students were assigned to two reading groups. Participants in the customised reading group read online English texts, each of which was customised by the developed system to offer immediate and repeated meetings with previously encountered unknown words, while participants in the typical reading group read online texts without control on unknown word recurrence. After the 4‐week online reading treatment, the two groups of learners were tested by online immediate and delayed vocabulary tests, and the students from the customised reading group were also required to complete the questionnaire regarding system use. The results showed that the vocabulary mean scores from both immediate and delayed testing demonstrated significantly better results in word gain and word retention with the customised reading group and that the adaptive reading system was appealing for the students. The study demonstrated that by providing customised reading with word recurrence specific to individuals' unknown words, this adaptive computer‐assisted English as a foreign language reading system creates a more favourable condition for foreign language vocabulary growth.     

Using frameworks to develop a distributed conferencing system: an experience report

Application frameworks are a powerful means to reduce software development costs while improving quality. However, at the same time they are difficult to select and understand, as well as hard to learn, use, and debug effectively and efficiently. In this paper we report the story of eConference, a distributed conferencing system that was developed as part of a broader research effort. Here we discuss the lessons learned from the evolution of our conferencing tool over four generations, which have been necessary to find good frameworks and build a flexible distributed tool. Copyright © 2009 John Wiley & Sons, Ltd.     

Worst‐case optimal control for an electrical drive system with time‐delay

This paper considers an optimal control developed for an electrical drive system with a DC motor. Since it is a linear control system with input time‐delay subject to unknown but bounded disturbances, we construct a worst‐case feedback control policy, which can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value at the given final time, and the cost functional takes the best guarantee value. The worst‐case feedback control policy is allowed to be corrected at a given set of correction points between the initial and the final time, which is equivalent to solving a (m‐1)‐level min‐max problem. Since the min‐max problem at each stage does not yield a simple analytical solution, construction of the optimal policy is computationally prohibitive. This is why we consider an approximate control policy which is more convenient for computation. The simulation results illustrate that this proposed approach is feasible. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Universal output feedback adaptive control with uncertain nonlinearity and unknown high‐frequency gain sign: A new design

In this paper, we propose a new universal output feedback adaptive controller to globally (or semiglobally) stabilize nonlinear output feedback systems, whose nonlinearities are bounded either by known functions with unknown parameters or by completely unknown functions. In addition, no a priori knowledge of the sign of high‐frequency gain is required. The new design focuses on properly arranging the control gains step by step in the filter backstepping design. Instead of Lyapunov‐based argument, an inductive contradiction argument is employed in the proof of stability, which is not common in literature.     

Observer‐based decentralized adaptive control for large‐scale pure‐feedback systems with unknown time‐delayed nonlinear interactions

This paper presents an approximation design for a decentralized adaptive output‐feedback control of large‐scale pure‐feedback nonlinear systems with unknown time‐varying delayed interconnections. The interaction terms are bounded by unknown nonlinear bounding functions including unmeasurable state variables of subsystems. These bounding functions together with the algebraic loop problem of virtual and actual control inputs in the pure‐feedback form make the output‐feedback controller design difficult and challenging. To overcome the design difficulties, the observer‐based dynamic surface memoryless local controller for each subsystem is designed using appropriate Lyapunov‐Krasovskii functionals, the function approximation technique based on neural networks, and the additional first‐order low‐pass filter for the actual control input. It is shown that all signals in the total controlled closed‐loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, simulation examples are provided to illustrate the effectiveness of the proposed decentralized control scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust adaptive quantized DSC of uncertain pure‐feedback nonlinear systems with time‐varying output and state constraints

In this paper, the problem of neural adaptive dynamic surface quantized control is studied the first time for a class of pure‐feedback nonlinear systems in the presence of state and output constraint and unmodeled dynamics. The considered system is under the control of a hysteretic quantized input signal. Two types of one‐to‐one nonlinear mapping are adopted to transform the pure‐feedback system with different output and state constraints into an equivalent unconstrained pure‐feedback system. By designing a novel control law based on modified dynamic surface control technique, many assumptions of the quantized system in early literary works are removed. The unmodeled dynamics is estimated by a dynamic signal and approximated based on neural networks. The stability analysis indicates that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the output and all the states remain in the prescribed time‐varying or constant constraints. Two numerical examples with a coarse quantizer show that the proposed approach is effective for the considered system.     

Output‐feedback robust adaptive backstepping control for a class of multivariable nonlinear systems with guaranteed tracking performance

This paper is devoted to output‐feedback adaptive control for a class of multivariable nonlinear systems with both unknown parameters and unknown nonlinear functions. Under the Hurwitz condition for the high‐frequency gain matrix, a robust adaptive backstepping control scheme is proposed, which is able to guarantee the tracking performance and needs only one parameter to be updated online regardless of the system order and input–output dimension. To cope with the unknown nonlinear functions and improve the tracking performance, a kind of high‐gain K‐filters is introduced. It is proved that all signals of the closed‐loop system are globally uniformly bounded. Simulation results on coupled inverted double pendulums are presented to illustrate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

In this work, we develop a robust adaptive fault‐tolerant tracking control scheme for a class of input‐quantized strict‐feedback nonlinear systems in the presence of error/state constraints and actuation faults. The problem is rather complicated yet challenging if nonparametric uncertainties and unknown quantization parameters as well as time‐varying yet completely undetectable actuation faults are involved in the considered systems. Compared with the most existing approaches in the literature, the proposed control exhibits several attractive advantages: (1) upon using a nonlinear decomposition for quantized input and employing the robust technique for actuation fault, not only the exact knowledge of quantization parameters are not required, but also the actuation fault can be easily compensated since neither fault detection and diagnosis/fault detection and identification nor controller reconfiguration is needed; (2) based on the error/state‐dependent unified nonlinear function, the constraints on tracking error and system states are directly handled and the cases with or without constraints can also be addressed in a unified manner without changing the control structure; and (3) the utilization of unified nonlinear function‐based dynamic surface control not only avoids the problem of the explosion of complexity in traditional backstepping design, but also bypasses the demanding feasibility conditions of virtual controllers. Furthermore, by using the Lyapunov analysis, it is ensured that all signals in the closed‐loop systems are uniformly ultimately bounded. The effectiveness of the developed control algorithm is confirmed by numerical simulations.     

Nonidentifier‐based adaptive control for nonlinearly parameterized systems with measurement uncertainty

For a family of nonlinear systems with parametric uncertainty in both state and output equations, we prove that global adaptive regulation is still achievable by output feedback. The bounds of the time‐varying parameter at the system output are unknown, and the class of nonlinear systems is assumed to be dominated by a triangular system that satisfies a linear growth condition with a polynomial output‐dependent rate. The result presented in this article has incorporated and generalized recent advances on robust output feedback control of nonlinear systems with output uncertainty, all of them are required to satisfy a linear growth condition with a constant rate. A nonidentifier‐based universal controller is proposed with a high gain estimator, rather than observer, whose gain is updated in a dynamic fashion. It is shown that a single dynamic gain is sufficient for dealing with the unknown parameter at the system output and the system parametric uncertainty simultaneously.     

Controlling the multi‐plant networked system with external perturbations via adaptive model‐based event‐triggered strategy

This paper concerns the multi‐plant networked control system with external perturbations by applying the adaptive model‐based event‐triggered control strategy. Compared with existing works, we introduce multiple plants topology in order to smooth the information exchanges among different plants. Gained insight into the adaptive model features in the view of event‐triggered thought, the event‐triggered rules, determining when the control inputs update, are obtained using the Lyapunov technique to reduce the communication cost. By designing some adaptive updating laws combined with the concept of event‐triggered, the unknown parameters in uncertain multi‐plant networked control systems are real‐time online estimated and adjusted with respect to the relevant nominal systems at event‐triggered instants. To avoid Zeno phenomena, a lower bound of event‐triggered execution interval is discussed. Furthermore, given the external perturbations, gain stability theory is introduced to analyze the stability of multi‐plant networked control systems with bounded perturbations, and then the sufficient conditions related to the multiple plants topology structure are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of our theoretic results.     

An adaptive event‐triggering scheme for networked interconnected control system with stochastic uncertainty

This paper investigates the problem of adaptive event‐triggering scheme for networked interconnected systems to relieve the burden of the network bandwidth. The data releasing is triggered by an adaptive event‐triggering device. The triggering condition depends on the state information at both the latest releasing instant and the current sampling instant. The threshold of the triggering parameter is achieved online rather than a predetermined constant. Taking the network‐induced delays and the coupling delays of the subsystems into account, together with the hybrid adaptive event‐triggering scheme and the stochastic uncertainty, we propose an unified model of the networked interconnected system. Sufficient conditions for the mean square stability and stabilization of the interconnected systems are developed by using Lyapunov–Krasovskii functional approach. A co‐designed method is put forward to obtain the controller gains and the weight of the triggering condition simultaneously. Finally, an example is provided to demonstrate the design method. Copyright © 2016 John Wiley & Sons, Ltd.     

EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter

This paper is concerned with application of expectation maximization (EM) algorithm for deriving an adaptive version of divided difference filter for joint state estimation and multiplicative parameter identification of nonlinear system with the colored measurement noise. Owing to the fact that there exist a mutual coupling and interaction of state and parameter on each other, it requires a joint or simultaneous estimation of both state and parameter by a mutual iteration, and justly, EM iterates Expectation (E‐)step and Maximization (M‐)step to meet such requirement. Firstly, E‐step involves state filtering and smoothing issues under knowing the previous parameter identification results, which is well solved by resorting to the Gaussian approximation with a trade‐off between accuracy and complexity. Further, such Gaussian approximation estimators are applied for evaluating the condition expectation of complete‐data likelihood function, nonlinearly characterized by the multiplicative parameter needed to be optimized. Secondly, M‐step deals with the maximization of the condition expectation by directly making its derivative as zero to obtain the current general parameter identification equation as the nonlinear integral. Thirdly, by iteratively operating E‐step and M‐step, an adaptive divided difference filter is proposed for joint state estimation and parameter identification by using the second‐order Stirling interpolation to compute the associated nonlinear integral. Finally, the robust performance of the EM‐based adaptive version of divided difference filter to the unknown or time‐varying multiplicative parameter, as compared with the standard augmentation method, is demonstrated by a maneuvering target tracking example. Copyright © 2016 John Wiley & Sons, Ltd.     

基于逆系统方法的广义非线性系统控制及电力系统应用

对一般广义非线性系统的反馈线性化问题,通过将代数变量视为虚拟输入,给出了构造性递归求逆算法,实现了系统的精确反馈线性化.作为该算法的应用,讨论了带有非线性负荷的三节点电力系统的励磁控制问题,设计了非线性励磁控制器.仿真结果表明了方法的有效性.     

Robust adaptive fault‐tolerant tracking control for a class of high‐order nonlinear system with finite‐time prescribed performance

In this article, an improved prescribed performance adaptive control strategy is developed to handle the output tracking control problem for a class of nonlinear high‐order systems with actuator faults. The actuator faults considered include the bias fault and gain fault models. A technique of adding a power integrator is utilized to deal with the controller design problem of high‐order system. With the help of backstepping technology and the classic adaptive control, an output tracking control scheme is proposed, which can guarantee that all signals of the closed‐loop system are bounded and the tracking error converges to a finite‐time predetermined region. Finally, the feasibility of the presented control method is tested through the simulation results.     

The velocity control of an electro‐hydraulic displacement‐controlled system using adaptive fuzzy controller with self‐tuning fuzzy sliding mode compensation

Hydraulic servo control systems have been used widely in industry. Within the realm of hydraulic control systems, conventional hydraulic valve‐controlled systems have higher response and lower energy efficiency, whereas hydraulic displacement‐controlled servo systems have higher energy efficiency. This paper aims to investigate the velocity control performance of an electro‐hydraulic displacement‐controlled system (EHDCS), where the controlled hydraulic cylinder is altered by a variable displacement axial piston pump to achieve velocity control. For that, a novel adaptive fuzzy controller with self‐tuning fuzzy sliding‐mode compensation (AFC‐STFSMC) is proposed for velocity control in EHDCS. The AFC‐STFSMC approach combining adaptive fuzzy control and the self‐tuning fuzzy sliding‐mode control scheme, has the advantages of the capability of automatically adjusting the fuzzy rules and of reducing the fuzzy rules. The proposed AFC‐STFSMC scheme can design the sliding‐mode controller with no requirement on the system dynamic model, and it can be free of chattering, thereby providing stable tracking control performance and robustness against uncertainties. Moreover, the stability of the proposed scheme via the Lyapunov method is proven. Therefore, the velocity control of EHDCS controlled by AFC‐STFSMC is implemented and verified experimentally in different velocity targets and loading conditions. The experimental results show that the proposed AFC‐STFSMC method can achieve good velocity control performance and robustness in EHDCS with regard to parameter variations and external disturbance. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptation provisioning with respect to learning styles in a Web‐based educational system: an experimental study

Personalized instruction is seen as a desideratum of today's e‐learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style‐based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates some of the limitations of similar systems. The adaptive methods used as well as their implementation in a dedicated system (WELSA) are presented, together with a thorough evaluation of the approach. The results of the experimental study involving 64 undergraduate students show that accommodating learning styles in WELSA has a beneficial effect on the learning process.     

Guiding students to the right questions: adaptive navigation support in an E‐Learning system for Java programming

Rapid growth of the volume of interactive questions available to the students of modern E‐Learning courses placed the problem of personalized guidance on the agenda of E‐Learning researchers. Without proper guidance, students frequently select too simple or too complicated problems and ended either bored or discouraged. This paper explores a specific personalized guidance technology known as adaptive navigation support. We developed JavaGuide, a system, which guides students to appropriate questions in a Java programming course, and investigated the effect of personalized guidance a three‐semester long classroom study. The results of this study confirm the educational and motivational effects of adaptive navigation support.     

Robust adaptive control for a class of semi‐strict feedback systems with state and input constraints

This paper proposes a dynamic surface control (DSC)–based robust adaptive control scheme for a class of semi‐strict feedback systems with full‐state and input constraints. In the control scheme, a constraint transformation method is employed to prevent the transgression of the full‐state constraints. Specifically, the state constraints are firstly represented as the surface error constraints, then, an error transformation is introduced to convert the constrained surface errors into new equivalent variables without constraints. By ensuring the boundedness of the transformed variables, the violation of the state constraints can be prevented. Moreover, in order to obtain magnitude limited virtual control signal for the recursive design, the saturations are incorporated into the control law. The auxiliary design systems are constructed to analyze the effects of the introduced saturations and the input constraints. Rigorous theoretical analysis demonstrates that the proposed control law can guarantee all the closed‐loop signals are uniformly ultimately bounded, the tracking error converges to a small neighborhood of origin, and the full‐state constraints are not violated. Compared with the existing results, the key advantages of the proposed control scheme include: (i) the utilization of the constraint transformation can handle both time‐varying symmetric and asymmetric state constraints and static ones in a unified framework; (ii) the incorporation of the saturations permits the removal of a feasibility analysis step and avoids solving the constrained optimization problem; and (iii) the “explosion of complexity” in traditional backstepping design is avoided by using the DSC technique. Simulations are finally given to confirm the effectiveness of the proposed approach.