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In this study, an active fault-tolerant control problem was addressed for a rigid spacecraft in the presence of unknown actuator faults, uncertainties, and disturbances. First, an adaptive sliding mode iterative learning-based observer (ASILO) is proposed for diagnosing and reconstructing unknown faults. It achieves greater accuracy and rapidity while consuming less computing resources by constructing adaptive gain based on an auxiliary error. Specifically, it significantly improved the computational efficiency by 76% compared with the Strong Tracking Kalman Filter while achieving a similar accuracy. It also enhanced the accuracies relative to the traditional ILO and adaptive ILO by 67% and 36%, respectively, and demonstrated 82% and 52% increases in rapidity. Then, fault-tolerant control with resilient prescribed performance (RPP) that can adapt to changing initial conditions and adaptively adjust performance constraints online by sensing faults and error trends is proposed. It avoided the control singularity by constructing adaptive resilient boundaries with almost no impact on the computational overhead. It significantly improved the performance and conservatism. Finally, the robustness and effectiveness of the proposed strategy were demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In offshore environments, safe management of heavy payloads requires precise crane operations to avoid collisions with obstacles and adjacent equipment. Uncontrolled residual swinging of suspended payloads can quickly evolve into high-risk situations, which, if left unchecked, might lead to significant equipment failures and associated costs. This paper explores a control methodology designed specifically to eliminate payload swing in offshore cranes. We present a trajectory tracking technique explicitly crafted for swing suppression under control, rooted in the principles of the iterative learning algorithm and based on physics. The proposed antiswing control strategy guarantees asymptotic convergence of the payload's swing, angular velocity, and angular acceleration to desired values. The method was tested on a Comau robot mounted on a Stewart platform at the Norwegian Motion Laboratory. Simulation and experimental results comparing payload transfers with and without applying the anti-swing control method validates it's effectiveness.

Introduction: The robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient–robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user's movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well. Methods: This study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human–robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system. Results: Experiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks. Discussion: The integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients. [ABSTRACT FROM AUTHOR]

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A model-free control method based on iterative learning law combined with adaptive super-twisting is proposed to realize the synchronous coordination control of multi-lift overhead crane system for the problems of inaccurate modeling, system parameter variation, and disturbance uncertainty that exist in multi-lift overhead crane system. First, a load-coupling model of the double-container overhead crane considering the deformation tangential force in the interlocking mode is established. Second, a time-varying sliding mode surface (TSMC) designed using nonlinear functions effectively improves the convergence speed of the system state. The method of iterative learning control is introduced to compensate the system dynamics to achieve model-free control, and the dynamic iterative learning control (DILC) is designed to improve the convergence speed of the error of the system and the steady-state performance. To suppress uncertainty disturbances and avoid control gain overestimation, an adaptive gain is added to the generalized super-twisting algorithm, which has the advantages of both finite-time convergence and chattering suppression, and improves the robustness and tracking performance of the multi-lift overhead crane system. The stability of the controlled system is analyzed using Lyapunov stability theory. The simulation experiments illustrate the effectiveness of the proposed synchronization control scheme. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between complexity, error and design, highlighting how the dynamic interaction between these elements is crucial in addressing the challenges of contemporary design. An interdisciplinary analysis investigates the role of error as a strategic resource in education and professional practice, and a methodology is proposed that systematises the management of error, encouraging the emergence of innovative and adaptive solutions. The importance of a flexible and open approach is emphasised, recognising error as a central element in stimulating innovation, broadening perspectives, improving design processes and fostering ongoing growth in the field. Article info Received: 10/09/2024; Revised: 12/10/2024; Accepted: 14/10/2024

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance. [ABSTRACT FROM AUTHOR]

The problem of design of control systems for electrohydraulic benches for strength tests and life tests of aircraft structures is considered. It is proposed to use a control algorithm with iterative learning to improve the accuracy of forming a given cyclogram of force loading of mechanical structures. The results of numerical simulation of the test bench control system with iterative learning and the results of experimental verification of the proposed algorithm on a single-channel test bench are given. The results of simulation and experiments on the test bench showed that the accuracy of forming a given cyclogram of force loading can be increased based on the proposed approach. The results allow automating the adjusting controllers for electrohydraulic drives of strength test benches and accelerating the process of carrying out life tests and static strength tests of aircraft structures. [ABSTRACT FROM AUTHOR]

IntroductionThe robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient–robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user’s movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well.MethodsThis study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human–robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system.ResultsExperiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks.DiscussionThe integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients.

In this paper, a novel neural network-based robust iterative learning fault estimation scheme is proposed to address the problem of fault modeling and estimation in nonlinear manipulator systems with disturbance and parameter uncertainties. The aim is to enhance the rapidity, efficiency, and accuracy of fault estimation. Firstly, the modeling for flexible manipulator control system is constructed as a preparation of iterative learning fault estimation observer design. Then, the neural network model is constructed to optimize the gain parameters of iterative learning fault estimator to approximate nonlinear uncertainties. Additionally, a H ∞ robust technique is used to suppress fault variation rate and disturbance, which enhances the speed of estimation and reduces the impact of disturbance. So that the estimated fault can rapidly and accurately track the actual fault over the whole time interval and iterations. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed neural network-based robust iterative learning scheme. [ABSTRACT FROM AUTHOR]

In this article, the problem of multiple fault detection, isolation and reconfiguration of the rolling mill main drive system containing external disturbances is investigated. Considering the nonlinear frictional damping between the rolls and the rolled parts, a nonlinear mathematical model of the main drive system of the mill is established. A comprehensive fault diagnosis scheme based on observer is addressed for this system subjected to unknown external interference. The proposed scheme is divided into two parts. In the first stage, a set of sliding mode observers is designed for system fault detection, and a fault isolation criterion is proposed based on observer redundancy and generalised residual set theory to reveal the fault source. In the second stage, combined with the iterative learning algorithm, an iterative learning-unknown input observer is constructed to realise the accurate estimation of the fault signal. Unlike the existing fault estimation methods, the iterative learning-unknown input observer designed in this article uses the state estimation error of the previous iteration to estimate the fault signal in the current iteration period. Using H ∞ synthesis to design observers for the system will guarantee fault diagnosis robustness. The Lyapunov theory and linear matrix inequality are introduced to prove the convergence of the proposed observer. The simulation study of a 1780-mm hot strip mill evaluates the proposed scheme. Simulation results demonstrate that the sliding mode observer approach can detect faults in the main drive system and isolate faults accurately. In contrast, the iterative learning-unknown input observer method has the lowest fault reconfiguration error (99.87% smaller than the extended state observer, 99.77% smaller than the unknown input observer) and achieves accurate fault signal tracking. [ABSTRACT FROM AUTHOR]

An accurate segmentation of the maxillary sinus (MS) is helpful for preoperative planning of dental implantation, diagnosis and evaluation of sinusitis, and validation of radiotherapy for sinus cancer. Many medical image segmentation models based on convolutional neural networks have achieved excellent performance, however, relied heavily on manual accurate labeling of training data. We propose an iterative learning method for MS segmentation with only bounding box supervision. First, a cone-beam computed tomography (CBCT) image is over-segmented into a set of superpixels and a feature extraction network is optimized to better extract multi-scale features of each small-size superpixel. Second, an improved graph convolutional network (IGCN) is developed to merge superpixel regions and improve the feature transformation ability of each node on a superpixel-wise graph. Finally, the iterative learning combined with the superpixel-conditional random field and IGCN makes pseudo labels gradually refine and close to fully supervised information. On a practical MS dataset, the proposed method achieves 90.5% in Dice similarity coefficient. Extending to the public dataset Promise12 for prostate MR image segmentation, it also performs well. The results show that our proposed method has good comprehensive weakly supervised segmentation performance and can narrow a gap between the bounding box and full supervision. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the large number of the power switches, high voltage and current stress, and double-line-frequency ripple on the dc-side current in the existing single-stage high-frequency isolated dc-ac converters, the novel single-stage isolated dc-ac topology is proposed in this paper. The interleaved parallel structures are used on both the dc and grid sides. The harmonic components of the currents in both the dc and grid sides are suppressed and the number of the power switches is reduced. Furthermore, based on the dual PWM method with the phase-shift modulation strategy, the operation principles, and characteristics of the proposed converter are analyzed thoroughly. In order to further achieve the suppression of the harmonic currents on both the dc and grid sides, the composite controllers based on the P-type iterative learning are proposed. By the composite controller, each harmonic current is effectively suppressed regardless of the harmonic frequency. The stability, convergence, and operation characteristics of the controllers are analyzed meticulously. The correctness and effectiveness of the proposed converter and closed-loop control strategy are validated through the experimental results.

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short. [ABSTRACT FROM AUTHOR]

Asymptotic output tracking of non‐minimum phase (NMP) nonlinear systems has been a popular topic in control theory and applications. Many approaches have focused on finding solutions under minimal assumptions either in the target system or desired trajectories, as there is no general solution available. In this article, we propose a practical and simple solution for cases where the reference trajectory is periodic in time. Our approach employs a learning‐based scheme to iteratively determine the desired feedforward input. Unlike previous learning‐based frameworks, our method only requires the output tracking error to update the feedforward input iteratively and can be applicable to NMP systems. Our method retains the key advantages of the learning‐based framework, including robustness to parameter uncertainties and periodic disturbances. We evaluate the effectiveness of our algorithm using simulation results with an inverted pendulum on a cart, a typical NMP nonlinear system. [ABSTRACT FROM AUTHOR]

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5 % , 45.8 % , 46.4 % and 39.8 % , respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7 × 10 − 4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks. [ABSTRACT FROM AUTHOR]

Periodic torque ripple often occurs in permanent magnet synchronous motors due to cogging torque and flux harmonic distortion, leading to motor speed fluctuations and further causing mechanical vibration and noise, which seriously affects the performance of the motor vector control system. In response to the above problems, a PMSM torque ripple suppression method based on SMA-optimized ILC is proposed, which does not rely on prior knowledge of the system and motor parameters. That is, an SMA is used to determine the optimal values of the key parameters of the ILC in the target motor control system, and then the real-time torque deviation value calculated by iterative learning is compensated to the system control current set end. By reducing the influence of higher harmonics in the control current, the torque ripple is suppressed. Research results show that this method has high efficiency and accuracy in parameter optimization, further improving the ILC performance, effectively reducing the impact of higher harmonics, and suppressing the torque ripple amplitude. [ABSTRACT FROM AUTHOR]

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one‐dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two‐dimensional PID controller optimized by two‐dimensional model predictive iterative learning control (2D‐PID‐MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two‐dimensional PID (2D‐PID) controller is developed by combining the advantages of a PID‐type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D‐PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one‐dimension to two‐dimensions. Second, the tuning guidelines of the presented 2D‐PID controller are obtained from the two‐dimensional model predictive control iterative control (2D‐MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D‐MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process. [ABSTRACT FROM AUTHOR]

In ultra-precision diamond turning, the reduction of machining form errors can generally be achieved through on-machine measurement and compensation. However, the efficiency of conventional compensation methods is often insufficient, particularly when high form accuracy is required or when intricate surface topography and microstructures need to be machined. Consequently, this research proposes a novel machining error compensation method based on iterative learning from on-machine measured data to enhance the machining accuracy and compensation efficiency. The on-machine measurement system and cutting path generation algorithm are introduced first. Then, the compensation method via iterative learning is presented theoretically, demonstrating a higher convergence order compared to the conventional method. Finally, machining experiments involving the cutting of cosine surfaces are conducted, followed by measurements of the processed workpieces. The experimental results indicate that after four rounds of compensation using the conventional method, the peak-to-valley (PV) value of the form error is reduced to 0.1134 μ m . In contrast, employing the proposed method, a similar value of 0.1156 μ m is achieved after only two rounds of compensation. This highlights the significant reduction in compensation time facilitated by the proposed method. Furthermore, the measurement results verify that the proposed compensation method maintains excellent surface quality. [ABSTRACT FROM AUTHOR]

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lithographic machine tools require both high motion accuracy and high motion flexibility. Projection based iterative learning control (P-ILC) is appealing for wafer stages to achieve two goals, simultaneously. P-ILC contains a nonparametric feedforward controller based on ILC, and a parametric feedforward controller with a projection step for feedforward tuning. In this paper, a set-membership based frequency-domain ILC algorithm (SM-F-ILC) is employed in the enhancing P-ILC scheme to improve the performance in the nonparametric feedforward control mode. SM-F-ILC can effectively compensate for repetitive errors, attenuate the nonrepetitive error accumulation and achieve fast convergence speed with model uncertainties. These superiorities also facilitate to improve the performance of P-ILC in the parametric feedforward control mode. The validity of the enhancing P-ILC scheme is demonstrated by experimental results. • The enhancing P-ILC scheme can achieve high motion accuracy and flexibility. • SM-F-ILC enables P-ILC high nonparametric and parametric feedforward performances. • The enhancing P-ILC scheme always contains a disturbance compensation scheme. [ABSTRACT FROM AUTHOR]

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM. • A iterative neural network scheme with network parameters and weights tuned via iterative learning is proposed to improve the uncertainty approximation capability. • The proposed scheme integrating of model-based and data-based method improves the tracking performance of the maglev planar motor. • The stability of maglev planar motor regulated by the proposed iterative neural network adaptive robust control method is proved via Lyapunov theory. [ABSTRACT FROM AUTHOR]

Safe autonomous driving approaches should be capable of quickly and efficiently learning as professional drivers do, while also using all of the available road-tire friction for safety. Inspired by how skilled drivers learn, we demonstrate improvement from an initial optimization-generated racing trajectory using model-based reinforcement learning. By using a simple physics-based dynamics model and gradients of the performance objective, we show that a full-scale automated race car is capable of improving lap time in experiments on high- and low-friction race tracks. Using recorded vehicle data, this approach improves a twenty nine second lap time by almost two full seconds. Beyond improving upon the initial optimization-based solution, it uses only two laps worth of ice track data where conditions can constantly change from lap-to-lap. These results suggest that by combining an approximate model with simple learning techniques, significant improvement to automated racing strategies is possible.

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ( $>$ 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.

The traffic flow system of urban road network has strong randomness and time-varying nature, and it is difficult for a single fixed traffic flow model to accurately describe the actual operation of urban road network. In order to describe the actual operation of traffic flow in urban road networks more accurately, a nonlinear macroscopic traffic flow model was proposed with unknown time-varying multi-parameter by taking into account the steady-state and dynamic characteristics of traffic flow, and a time-varying multi-parameter iterative learning identification strategy was designed by using the inherent repetitive characteristics of traffic flow. In the finite time interval, the iterative learning identification strategy is used to transform the parameter identification problem into an optimal tracking control problem, so that the number of queued vehicles at the entrance of each intersection converges on the true value, the real-time adaptive ability of unfalsified control algorithm is used to adjust the learning law gain of the iterative learning identification strategy, which improves the anti-interference ability of the identification strategy. The convergence of the algorithm is proved by a rigorous mathematical theoretical derivation, and finally the effectiveness of the method was further verified by simulation experiments using the model-based control method.

In order to improve the anti-disturbance ability and reduce the sensitivity to faults for the quadrotor UAV, the robust sliding mode adaptive control strategy based on iterative learning is designed in this paper. Firstly, the dynamic model of the quadrotor UAV is established. Secondly, the iterative learning observer is designed to track the states of the UAV system, and the convergence analysis of the observer is given. Thirdly, for the attitude system of the quadrotor UAV, a robust adaptive sliding mode control strategy based on iterative learning is proposed, and the stability is proved by the Lyapunov theory. Finally, by the simulation for the UAV attitude system, the validity of the iterative learning observer and robust adaptive sliding mode control strategy based on iterative learning is verified. [ABSTRACT FROM AUTHOR]

This paper presents an iterative training framework with a binary classifier to improve the learning capability of a deep learning model for detecting abnormal behaviors in surveillance video. When a deep learning model trained on data from one surveillance video is deployed to monitor another video stream, its abnormal behavior detection performance often decreases significantly. To ensure the desired performance in new environments, the deep learning model needs to be retrained with additional training data from the new video stream. Iterative training requires manual annotation of the additional training data during the fine-tuning process, which is a tedious and error-prone task. To address this issue, this paper proposes a binary classifier to automatically label false positive data without human intervention. The binary classifier is trained on bounding boxes extracted from the detection model to identify which boxes are true positives or false positives. The proposed learning framework incrementally enhances the performance of the deep learning model for detecting abnormal behaviors in a surveillance video stream through repeated iterative learning cycles. Experimental results demonstrate that the accuracy of the detection model increases from 0.35 (mAP = 0.74) to 0.91 (mAP = 0.99) in just a few iterations. [ABSTRACT FROM AUTHOR]

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ($>$ 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments. [ABSTRACT FROM AUTHOR]

Piezoelectric print-heads (PPHs) are used with a variety of fluid materials with specific functions. Thus, the volume flow rate of the fluid at the nozzle determines the formation process of droplets, which is used to design the drive waveform of the PPH, control the volume flow rate at the nozzle, and effectively improve droplet deposition quality. In this study, based on the iterative learning and the equivalent circuit model of the PPHs, we proposed a waveform design method to control the volume flow rate at the nozzle. Experimental results show that the proposed method can accurately control the volume flow of the fluid at the nozzle. To verify the practical application value of the proposed method, we designed two drive waveforms to suppress residual vibration and produce smaller droplets. The results are exceptional, indicating that the proposed method has good practical application value. [ABSTRACT FROM AUTHOR]