Struggle Between Accuracy and Fluency in Teaching Online

The advent of second-generation intelligent computer tutors raises an important instructional design question: when should tutorial advice be presented in problem solving? This paper examines four feedback conditions in the ACT Programming Tutor. Three versions offer the student different levels of control over error feedback and correction: (a) immediate feedback and immediate error correction; (b) immediate error flagging and student control of error correction; (c) feedback on demand and student control of error correction. A fourth, No-tutor condition offers no stepby-step problem solving support. The immediate feedback group with greatest tutor control of problem solving yielded the most efficient learning. These students completed the tutor problems fastest, and the three tutor-supported groups performed equivalently on tests. Questionnaires revealed little student preference among the four conditions. These results suggest that students will need explicit guidance to benefit from learning opportunities that arise when they have greater control over tutorial assistance.

Design of fixed (non-adaptive) neural controller for nonlinear plants described by input-output representation has been presented. The control scheme is based on an error plus output feedback. Integrators are used in the control loop to ensure set point following. A training scheme for the neural controller is derived that directly minimizes a versatile loss function. The training is conducted off line for a class of set points conforming to the normal operating condition of the plant. Results of simulation studies are presented.

In this manuscript, we address discrete-time state and error feedback output regulator designs for a class of linear distributed parameter systems (DPS) with bounded input and output operators. By utilising the Cayley–Tustin bilinear transform, a linear infinite-dimensional discrete-time system is obtained without model spatial approximation or model order reduction. Based on the internal model principle, discrete state and error feedback regulators are designed. In particular, discrete Sylvester regulator equations are formulated, and their solvability is proved and linked to the continuous counterparts. To ensure the stability of the closed-loop system, the design of stabilising feedback gain and its dual problem of stabilising output injection gain design are provided in the discrete-time setting. Finally, three simulation examples including a first-order hyperbolic partial differential equation model and a 1-D heat equation with considerations of step-like, ramp-like and harmonic exogenous signals are shown to demonstrate the effectiveness of the proposed method.

We investigate the efficient implementation of continuous time output feedback nonlinear model predictive control (NMPC) using a moving horizon observer (MHE) for state estimation. For this purpose we briefly review some aspects of MHE and some results on the computationally efficient implementation of nonlinear model predictive control. For the MHE we outline how an efficient dynamic optimization strategy for NMPC, the so called real-time iteration scheme, can be suitably adapted leading to a computationally efficient MHE implementation. The scheme is based on the multiple shooting method, a well known robust technique for nonlinear dynamic estimation problems. Application of the scheme allows us to use the MHE based output feedback NMPC controller in practice for reasonably sized problems. We exemplify the performance and computational demand of the proposed scheme by a simulation study considering the control of a high-purity distillation column.

Multivariable uniform finite-time output feedback reentry attitude control for RLV with mismatched disturbance

In this paper we develop a nonsmooth output feedback framework for local and/or global stabilization of a class of inherently nonlinear systems, which may not be achieved by any smooth feedback controller. A systematic design algorithm is presented for the construction of stabilizing, dynamic output compensators that are nonsmooth but Holder continuous. A new ingredient of the proposed output feedback control scheme is the introduction of a recursive observer design procedure, making it possible to construct a reduced-order observer step-by-step, in a naturally augmented fashion. Such nonsmooth design leads to a number of new results on output feedback stabilization of nonlinear systems with unstabilizable/undetectable linearization. One of them is the global stabilizability of a chain of odd power integrators by Holder continuous output feedback. The other one is the local stabilization using nonsmooth output feedback for a wide class of nonlinear systems studied in Qian and Lin (2001), where only global stabilizability by nonsmooth state feedback was proved to be possible.

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition

In this paper we propose a model reference adaptive control strategy using output feedback. When the control system is constructed using the proposed controller, the maximum value of the output error decreases as the value of the feedback gain of the output error increases. Moreover, the proposed control system has good performance that the maximum value of the control input does not increase even if the value of feedback gain increases and that high-freqency vibrations have difficulty appearing in the control input. A control system having such properties would be very useful for controlling actual plants.

Attractivity of unstable equilibria for a controlled Chen system via small output feedback

In the realm of global smart education, optimizing classroom learning efficiency is paramount. This study harnesses cutting-edge technology to delve into learning engagement dynamics. Leveraging the XGBoost model, 468 facial and bodily features are captured in real-time, offering insights into student behavior. The ResNet neural transformation network and PNP algorithm discern facial expressions, body movements, and head rotation angles. Through a fuzzy comprehensive evaluation method, proportions of these factors are delineated for objective assessment. Empirical validation involves scrutinizing final exam scores of 234 college students. Comparative analyses reveal a significant correlation between classroom participation and performance (p-value = 0.034). Results underscore the impact of real-time judgment on learning efficiency and highlight the potency of multimodal fusion in assessing engagement comprehensively. Practical application validates system effectiveness, offering insights for educational stakeholders and contributing to the evolution of smart education.

One of the most difficult determinations a psychiatrist makes is whether the chronically suicidal patient is at risk for imminent suicide. The Introduction presents the challenges of the imminent risk assessment and explains the purpose of The Suicidal Narrative—the first clinical guide to such an assessment. It introduces the distinction between long-term and short-term suicide risk, highlights the lack of structured instruments for imminent risk assessment, and discusses the advantages of multimodal and multi-informant assessment methods. It then describes intended uses of the guide, lists its inherent limitations, and identifies possible areas of unintended misuse. The Introduction concludes by naming the main intended use of The Suicidal Narrative: as a roadmap for a comprehensive assessment of imminent suicide risk in diverse clinical settings.

This paper addresses the problems of output feedback stabilization for a class of nonlinear systems with sampled and delayed measurements. By constructing a continuous observer, a sampled-data output feedback control law is presented to globally exponentially stabilize the nonlinear systems. Transmission delays are also taken into consideration in the observer design and the controller design. The maximum allowable delay is also given. Finally, an illustrative example is presented to show the effectiveness of the proposed methods.

This paper investigates the problem of global output feedback stabilisation for a class of high-order nonlinear systems with multiple time-varying delays. By using backstepping recursive technique and the homogeneous domination approach, a continuous output feedback controller is successfully designed, and the global asymptotic stability of the resulting closed-loop system is proven with the help of an appropriate Lyapunov– Krasovskii functional. Two simulation examples are given to illustrate the effectiveness of the proposed approach.

In this paper, a new continuous output feedback control mechanism is developed for output tracking for a class of high-order multi-input nonlinear systems with an input gain matrix that is positive definite but non-symmetric. The controller yields semiglobal uniformly ultimately bounded (SGUUB) tracking while compensating for unstructured uncertainty in both the drift vector and the input matrix. First, a fullstate feedback controller is designed based on limited assumptions on the structure of the system nonlinearities and the controller is proven to yield SGUUB tracking through a Lyapunov-based analysis. Then, an output feedback control design based on a high gain observer is proposed. A comprehensive stability analysis of the closed-loop system under output feedback is carried out and a recovery of the state feedback SGUUB result is demonstrated for the output feedback control system. Neural network estimation method is employed in both state and output feedback control design to feedforward compensate for the nonlinear system uncertainty.

In this letter, we present an online learning mechanism called the dual integral learner for fast frequency adaptation in neural central pattern generator (CPG) based locomotion control of a hexapod robot. The mechanism works by modulating the CPG frequency through synaptic plasticity of the neural CPG network. The modulation is based on tracking error feedback between the CPG output and joint angle sensory feedback of the hexapod robot. As a result, the mechanism will always try to match the CPG frequency to the walking performance of the robot, thereby ensuring that the entire generated trajectory can be followed with low tracking error. Real robot experiments show that our mechanism can automatically generate a proper walking frequency for energy-efficient locomotion with respect to the robot body as well as being able to quickly adapt the frequency online within a few seconds to deal with external perturbations such as leg blocking and a variation in electrical power. These important features will allow a hexapod robot to be more robust and also extend its operating time. Finally, the generality of the mechanism is shown by successfully applying it to a compliant robotic manipulator arm called GummiArm.