This paper investigates an efficient output feedback model predictive control (EOFMPC) for the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy system over communication networks utilizing a novel quantization-dependent Lyapunov function (QDLF). First, an adaptive event-triggered (AET) mechanism is designed to alleviate the communication resources, and the static logarithmic quantizer is used to reduce the network load, while a unified closed-loop model for the considered networked control system (NCS) is established. Second, a state observer is designed to estimate the unmeasurable system states, and an additional optimization problem is solved to obtain a smaller estimation error set. Third, a novel stability condition of the NCS is provided by introducing the QDLF, which significantly reduces the conservatism of the results. Moreover, an EOFMPC framework for the NCS is proposed, which consists of two main components: offline computation of the initial invariant set region resulting from the intersection of two ellipsoids, and the online optimization of the perturbation variable which is reformulated as a new convex formulation by introducing matrix partitioning technique. Finally, the effectiveness of the method is validated through simulation examples and comparative studies.

For the purpose of better inhibiting the chattering phenomenon and elevating the control performance of the magnetic levitation system under multiple disturbances, a continuous integral terminal sliding mode controller (CITSMC) based on generalized disturbance estimation (GDE) is put forward in this paper. Firstly, stemming from the mechanism modeling approach, an approximate nonlinear model of the magnetic levitation ball system is established, and then the integral terminal sliding mode controller (ITSMC) is designed to guarantee finite-time convergence. Due to the system’s vulnerability to multiple external disturbances, the switching gain needs to be selected at a larger value, which will lead to serious chattering problem. As a result of these concerns, a generalized disturbance estimator is proposed, which can effectively estimate and compensate for the multiple disturbances and reduce chattering by selecting a smaller switching gain value. To further eliminate the negative influence of chattering, the super-twisting reaching law is introduced in the design process of ITSMC. The proposed composite controller has the advantages of being chattering-free and having robust anti-interference capabilities. Computer simulations and experiments are employed to double-check the effectiveness and ascendancy of the proposed strategy.Note to Practitioners—Magnetic levitation systems are subject to complicated external multiple disturbances and internal uncertainties as typical open-loop unstable systems with nonlinearity. As a consequence, developing a powerful controller for magnetic levitation systems is a challenging proposition. Firstly, aiming at the asymptotic convergence problem of traditional sliding mode, an integral terminal sliding mode controller is devised to realize finite-time convergence and avoid singularity. Secondly, a generalized disturbance estimator has been proposed for estimating and compensating for multiple disturbances. Finally, for the chattering problem, a continuous integral terminal sliding mode controller combined with super-twisting is designed to effectively eliminate the adverse effects of chattering. Simulation and experimental results verify the effectiveness and superiority of the proposed method.

The article investigates the speed regulation of permanent magnet synchronous motor (PMSM) systems. Existing control methods of the non-cascade structure suffer from the drawbacks of unsatisfactory anti-disturbance performance and slow convergence rate when the system is affected by disturbances, especially unmatched disturbances. Meanwhile, the requirements of current constraint and fast dynamics cannot be effectively balanced in the single-loop structure of speed and current using traditional control methods such as the PID controller. Because large transient currents induced by fast dynamics may damage the hardware of the system. Therefore, a current-constrained finite-time control approach is proposed. Specifically, a robust finite-time control scheme is developed with the assistance of the improved finite-time observer technique. The proposed method is capable of actively suppressing both matched and unmatched disturbances in non-cascade control systems. Simultaneously, an effective penalty mechanism is established to incorporate a specific gain function into the designed controller. This approach restricts the q-axis current to a predefined safe range without solving an optimization problem. Finally, comparative experiment results indicate that the newly proposed finite-time control method outperforms the baseline control methods in terms of disturbance rejection, convergence rate, and current constraint.

The parallel branches with independent optimized classification and localization capabilities are widely used in single-stage object detection. Defects such as feature conflicts, low level of information interaction, and empirical sample allocation scheme lead to weak spatial consistency of the outputs from different branches. In this work, we propose a Progressive Decoupled Task Alignment (PDTA) that enhances the information interaction between tasks while reducing the degree of feature coupling, and adopts a strategy based on sample screening and learning to achieve task alignment. First, we design the Discrepant Feature Decoupling Module (DFDM) embedded with the novel Oriented Decoupling Convolution (ODC) for the coupled features of the shared input, and the features extracted by ODC are utilized for disentanglement through the feed-in scheme with differences. Second, the Probabilistic Mapping Interaction Head (PMI-Head) utilizes the probabilistic mapping method to enhance task-specific semantics by information interaction. Finally, the network’s common attention to the content and position of the target is enhanced through the metric in the proposed Relevance-Guided Adaptive Task Alignment (RATA), in which an exponentially decaying manner is used to preserve the training samples that are more efficient for both tasks. During training, task-aligned learning is performed by Relevance-Guided Loss. Experiments on MS COCO and DIOR datasets demonstrate the effectiveness of our method, PDTA achieves better performance for object detection.

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

Feature Pyramid (FP) is typically a fundamental component for detecting multi-scale objects. However, as the network deepens, FP faces two problems: (1) Information loss caused by channel reduction. (2) The insufficient effective receptive field due to convolution with the sliding window mode. We found that the above problems can be alleviated by increasing the semantics extraction weights of the off-centered feature map. In this paper, a new feature pyramid architecture named Off-Centered Semantics Refinement Feature Pyramid Network (OSR-FPN) is proposed. Specifically, OSR-FPN contains two components exploiting the Off-Centered Semantics Refinement (OSR) mechanism: Features Supplement Module (FSM) and Receptive Field Enlargement Module (RFEM). FSM and RFEM are respectively designed to complement the lost context at the highest pyramid level and enrich the semantics by expanding the receptive field. In addition, we propose the Sigmoid-interpolation Padding method to enhance our OSR. Experiments on MS COCO dataset and UAVDT object detection benchmarks demonstrate the effectiveness of our method. As a result, OSR-FPN achieves a better accuracy of complex object detection.

A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and cannot cope with mismatched time-varying disturbances due to its inherent structure property. For this reason, two HOSMOs are devised to estimate the time-varying disturbances on the link and motor side, respectively. Then, by fusing the states and disturbance estimations into a novel terminal sliding-mode surface, a continuous robust FTC scheme is developed. The proposed control strategy can not only handle both matched and mismatched time-varying disturbances but also obtain a finite-time convergence performance. The rigorous finite-time stability analysis of the closed-loop system under the proposed control method is guaranteed. The results are illustrated to verify the effectiveness and robustness of the proposed design approach.

Task space beyond 2D planes brings challenges to the traditional automated guided vehicle (AGV) on goods delivery. In this paper, a novel self-balancing platform is designed for the stable stair climbing of AGV. While the AGV is moving in a constructional site, this mechanism enhances the AGV to handle heavy payload or even carry another operating robot, through keeping them horizontal to the ground over time. In the design, the two degree-of-freedom (DOF) of this mechanism allows the platform to rotate around two mutually perpendicular axes. The rotation ranges are [-15°, 15°] and [0°, 40°], respectively. Through the feedback of an inertial measurement unit (IMU), the self-balancing control can be achieved. The demonstration show that the workspace not only allows the mechanism to reduce the vibration acting on the carried goods when the AGV is driving on uneven terrain but also allows the platform to maintain the center of gravity (CG) while the AGV is climbing.

A robust trajectory tracking control problem for robots with flexible-joint (FJ) is presented in this work. Aiming to obtain a better trajectory tracking performance and reject the adverse impacts of unknown matched and mismatched time-varying disturbances on FJ robotic systems, a robust finite-time control approach based on a terminal sliding mode control (TSMC) method and two finite-time observers (FTOs) is proposed. First, the FTOs are utilized to estimate the matched disturbance and the mismatched disturbance, respectively. With the aid of these estimations, a novel terminal sliding surface is constructed. Then, the proposed robust TSMC scheme is developed, which possesses a property against the matched and mismatched time-varying disturbances in a finite-time. The results are given to verify the effectiveness of the proposed control approach.

In this paper, a new robust position controller is proposed for Series Elastic Actuators (SEAs) by using Sliding Mode Control (SMC) and a second order Disturbance Observer (DOb). The latter estimates not only disturbances but also their first and second order successive derivatives. A simple yet efficient dynamic model of the position control system is derived by using the analogy of a two-mass-spring-damper system. It is of fourth order and suffers from collocated and non-collocated disturbances. The former is directly cancelled by feeding-back its estimation through control input. The latter is suppressed by treating the estimations of disturbances and their first and second order time derivatives in the design of the SMC-based robust position controller. By cancelling disturbances via their estimations, not only the robustness of the position control system is improved but also the control signal chattering is lowered. The proposed robust controller significantly improves the position control performance of SEAs by suppressing plant uncertainties and external disturbances, such as friction, backlash, inertia variation and load. The validity of the proposed robust position controller is verified by giving experimental results of an SEA.