In practical applications, tower cranes often operate in outdoor environments and are subject to a variety of disturbances. Additionally, the control performance is significantly affected by uncertain dynamics, such as frictional forces and payload mass. To address these challenges, this paper presents a novel adaptive fuzzy control mechanism for nonlinear 5-degree of freedom (5-DOF) tower cranes with variable cable length. The proposed fuzzy adaptive mechanism leverages fuzzy logic to compensate for dynamic uncertainties and external disturbances thereby enhancing the overall robustness of the controlled system. Moreover, to ensure safe operation and avoid collisions, auxiliary constraint terms are introduced to restrict the actuated state variables within predefined bounds throughout the transportation process. In particular, an adaptive law is developed to accurately estimate the payload mass， which is critical for precise cable positioning. To the best of our knowledge, this work proposes a novel adaptive fuzzy closed-loop control framework for 5-DOF tower crane systems with varying cable lengths. The framework systematically integrates online payload mass estimation, state variable constraints, and fuzzy adaptive compensation to improve system stability and robustness. Based on the Lyapunov technique and Barbalat’s lemma, the system stability is theoretically analyzed and proven. Finally, the effectiveness of the proposed controller is validated by hardware experiments.

This paper proposes a novel energy-efficient control method for vehicle suspensions that addresses key application-oriented issues, including inevitable disturbance, inherent nonlinearity, state-coupling effects, and energy consumption. Unlike conventional schemes, the proposed method explicitly quantifies the influence of disturbance and state-coupling by designing dedicated effect indicators and exploiting the beneficial aspects of nonlinear dynamics. Positive effects are harnessed, while negative effects are transformed into advantageous contributions, thereby delivering superior robustness, lower energy consumption, and improved response rapidity. Specifically, a fuzzy disturbance observer is developed to accurately estimate the disturbance factors, including both parametric/unmolded uncertainty and external disturbance. Effect indicators are introduced to characterize the positive and negative impacts of disturbance and coupling on the active suspension system, and these insights are incorporated into the design of a novel adaptive controller. In addition, biologically inspired nonlinear reference model is deliberately introduced to make use of favorable nonlinear stiffness and damping effects. Furthermore, Lyapunov’s theory is employed to ensure the asymptotic stability of the overall suspension system. Experimental validation demonstrated that the proposed approach achieves excellent transient performance and significant energy savings, with reductions of up to 80% or more.

In practice, many mechanical systems are underactuated, such as naval vessels, cranes, and helicopters, to reduce energy consumption and enhance flexibility. However, compounded by strong nonlinearity arising from state coupling, the underactuated nature and high-order unavailable states pose great challenges to motion control (particularly for unactuated states lacking independent actuators or kinematic constraints). In this article, an adaptive controller based on fully-actuated system methods is proposed, together with a general and extensible analysis method. First, a group of high-order auxiliary variables, consisting of actuated/unactuated states, their derivatives, and proportional-differential terms, are designed to rearrange the nonlinear underactuated system as a high-order linear fully-actuated system without any linearization operations. The asymptotic convergence of auxiliary variables theoretically eliminates the steady-state errors of actuated/unactuated states together. For high-order unmeasurable variables, they are recovered by the constructed neural network observer to estimate high-order dynamics, which avoids discontinuous robust terms and improves the accuracy of compensation/positioning. Motivated by the inherent features and advantages of fully-actuated systems, this article proposes the first fully-actuated system-based continuous adaptive controller for a class of underactuated robots. Moreover, it is convenient to extend the proposed controller to handle more practical problems, such as state delay, without the need to reconduct Lyapunov-based analysis. In addition to complete theoretical frames, this article also provides several experimental validation.

In the study of dialogue system, personalized dialogue mainly focuses on the semantic matching degree between response and role. However, various factors, such as semantic style, dialogue noise, and sparse personality information, can affect the performance of the model. For this purpose, we construct a novel personalized dialogue model. Based on the personality description information and conversation history, it uses the information enhancement algorithm to cluster the personality description text into a number of fine sparse categories, and uses the feature classifier to precisely select the features highly relevant to the current dialogue situation according to the input query content. At the same time, the history information selector will fine-filter the conversation history, retaining the parts that are valuable for generating replies. We combine the processed personality description text with the filtered historical context information, and send it to the decoder through the feature cue learning strategy for deep processing to generate personalized responses. Our model integrates the proposed algorithms, classifiers, selectors and feature cue learning strategies to build a complete dialogue system. Experiments on two datasets show that our model is superior to other models in terms of consistency and coherence.

The exploration of energy-efficient active suspension control strategies for high-performance vibration suppression remains a critical challenge, particularly under partial-state measurements, uncertain dynamics, and external disturbances. This article proposes an innovative predefined-time output feedback control scheme for active vehicle suspension systems that achieves superior vibration mitigation with reduced energy consumption. By employing the time-varying scaling function technique, a predetermined-time extended state observer is developed to estimate unmeasurable velocities and lumped disturbance, while a second-order predefined-time filter is designed to avoid the explosion of computational complexity. Furthermore, using the effect characterization method and theX-mechanism reference dynamics, beneficial couplings/disturbances and nonlinearities can be reserved instead of direct cancellation, which leads to significant energy conservation up to 58% compared to other methods. Then, the predefined-time output feedback control is proposed to ensure that settling time can be arbitrarily user-specified using only one parameter, which is independent of initial conditions and control gains. Comparative experiments are performed to present the effectiveness and robustness of the proposed control method.

In this paper, a novel periodic sliding mode control method is designed for 4-DOF tower crane systems with unmatched disturbances as well as unknown/time-varying control directions. Specifically, the nonlinear disturbance observer is constructed to solve the unmatched uncertainties. The problems arising from unknown/time-varying control directions are tackled through the period sliding mode technique. Unlike most previous unknown control direction-related studies, the control coefficient is allowed to cross 0 in a continuous way. In addition, to improve the payload swing suppression and elimination performance, nonlinear terms involving payload swing information are elaborately injected into the control design. As far as we know, the designed periodic sliding mode control scheme provides the first control method for crane systems to successfully guarantee positioning as well as anti-swing performance in spite of unmatched disturbances and unknown control directions. The rigorous theoretical analysis is presented through Lyapunov techniques. Several simulations and experimental results are carried out to illustrate the merits of the designed periodic sliding mode control method.

Active vehicle suspension systems exhibit substantial vibration isolation capabilities, however, suffer from external disturbances, high energy consumption, risks of fault signals, limited transient performance, etc. In this paper, a predefined-time fault-tolerant control scheme is proposed for active suspensions to improve ride comfort and reliability, and enhance energy conservation. The reference X-dynamics together with a conditional disturbance cancellation scheme are developed to avoid the cancellation of beneficial nonlinearities and beneficial disturbances, respectively, which can reduce energy consumption without any optimization calculation or hardware alteration. Importantly, the error signals can converge to a predefined bound within the predefined time interval. Both the settling time and the residual bound can be arbitrarily user-defined, which are independent of initial states and control gains. Especially, to avoid singularity and alleviate chattering, a continuous piecewise function and a quadratic fraction inequality are constructed. The utilization of the proposed predefined-time fault-tolerant control facilitates satisfactory ride comfort with low energy cost. Experimental results are presented to validate the superior control performance of the designed control scheme.

This article presents a robust finite-time command-filtered backstepping control strategy for flexible-joint robotic systems with only position measurements. To the best of our knowledge, this method is proposed for the first time and applied to the flexible-joint robot (FJR) system subject to matched/mismatched disturbances. Herein, two finite-time disturbance observers (FTDOs) are adopted to reconstruct unmeasurable system states and total matched and mismatched disturbances. By combining a finite-time command-filtered backstepping controller with two FTDOs, a novel robust command-filtered backstepping controller is presented. Meanwhile, the practically finite-time stability analysis of the closed-loop system is rigorously presented by the Lyapunov function. Finally, numerical simulations and experimental studies are carried out for the FJR, whose results show the superiority of the proposed scheme in comparison with the existing approaches, such as the FTCFBC, the ADRC, and the CSMC.

A novel control method of addressing coupling and disturbance influences for finite-time energy-saving robust control of active suspension systems (ASSs) is investigated. By elaborately constructing coupling and disturbance effect indicators, the pros and cons of coupling and disturbance influences on ASSs are discussed, and then a finite-time coupling and disturbance effects-triggered control method is designed via a second-order sliding mode control technique. Importantly, the good/bad coupling effects are assessed through a well-designed nonlinear function. By means of determining if the sign of disturbances conforms to the expected motion or not, the addition of beneficial disturbance effects or removal of detrimental disturbance effects is implemented. Noticeably, by employing a bioinspired nonlinear reference model, beneficial nonlinear stiffness and damping effects are thus utilized, leading to the possibility of energy-saving performance. As a result, the proposed control method exhibits a unique feature, i.e., fully employing potential contribution from the coupling and disturbance effects, and presents a totally new coupling and disturbance effects-triggered control framework, leading to obvious performance improvement. Benchmark experimental conclusions are devoted to distinguishing the advantages and effectiveness of the designed tracking method.

Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field for identifying the long-tail, data-poor genre based on the related music audio segments, which is very prevalent in real-world scenarios. Most of the existing models are designed for class-balanced music datasets, resulting in poor performance in accuracy and generalization when identifying the music genres at the tail of the distribution. Inspired by the success of introducing Multi-instance Learning (MIL) in various classification tasks, we propose a novel mechanism named Multi-instance Attention (MATT)1to boost the performance for identifying tail classes. Specifically, we first construct the bag-level datasets by generating the album-artist pair bags. Second, we leverage neural networks to encode the music audio segments. Finally, under the guidance of a multi-instance attention mechanism, the neural network-based models could select the most informative genre to match the given music segment. Comprehensive experimental results on a large-scale music genre benchmark dataset with long-tail distribution demonstrate MATT significantly outperforms other state-of-the-art baselines.1Github: https://github.com/JohannesLiu/Music-Genre-Classification

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.

A novel adaptive neural network tracking control method is systematically investigated for a unique double-pendulum tower crane system model in this article. Several critical and practical application-oriented control issues, including robustness, tracking error limitation, double-pendulum effects, and input dead zone nonlinearity, are considered simultaneously, which have never been well addressed in the existing literature. Technically, neural networks are employed to approximate the functions with uncertain/unknown dynamics and nonideal inputs. Several barrier Lyapunov functions are proposed that can circumvent the violation of tracking error limitations in the proposed control method. Importantly, based on the designed adaptive neural network tracking control method, the jib and trolley can track their desired trajectories very fast, and the hook and payload sway can be completely eliminated. The Lyapunov stability theory and Babalat’s lemma are utilized to theoretically prove the convergence and stability of the proposed control system. Finally, well-designed simulation studies are carried out to verify the excellent performance and strong robustness of the control method. This article should be the first work considering a double-pendulum tower crane system with guaranteed convergence and performance without any linearization for the original nonlinear dynamic model.

Active suspension systems are widely used in vehicles to improve ride comfort and handling performance. However, existing control strategies may be limited by various factors, including insufficient consideration of different operation conditions, such as changing in vehicle mass, defects in strategy design leading to incapability for guaranteeing finite-time stability, lack of considering input effects of dead zone and saturation, excessive energy cost, etc. Importantly, very few results considered the energy-saving performance of active suspension systems although a well-perceived issue in practice. An adaptive fuzzy SMC method based on a bioinspired reference model is established in this article, which is to purposely address these problems and be able to provide finite-time convergence and energy-saving performance simultaneously. The proposed control method effectively utilizes beneficial nonlinear stiffness and nonlinear damping properties that the bioinspired reference model could provide. Therefore, superior vibration suppression performance with less energy consumption and improved ride comfort can all be obtained readily. By using a fuzzy-logic system (FLS), the proposed method is beneficial in compensating for system parameter uncertainties, external disturbances, input dead zones, and saturations. Furthermore, based on the adaptive PD-SMC method, the tracking errors can converge to zeros in finite time. The stability of the equilibrium point of all the states in active suspension systems is theoretically proven by Lyapunov techniques. Finally, simulation results are provided to verify the correctness and effectiveness of the proposed control scheme.