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Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy. • In the framework of IMM, a novel approach is proposed to deal with the uncertainty of error covariance and state noise. • The transition probability matrix is computed with recursive maximum likelihood method to adapt to different noise characteristics. • The Square-root CKF based on inertial navigation error model and measurement model is introduced. • The vehicle navigation experiments with different maneuvers are designed and implemented. [ABSTRACT FROM AUTHOR]

Offshore boom cranes are complex nonlinear underactuated systems, whose control problems, when considering the 3-dimensional (3-D) model affected by ship-induced disturbances, are full of various challenges. In fact, it still remains an open problem to efficiently control the 3-D offshore boom cranes. Moreover, most existing works on offshore cranes concentrate upon designing the force/torque controller, whose performance degrades badly in the presence of friction. In this paper, considering the above issues, a novel model predictive control (MPC) method, which successfully considers the constraints of both input and output of the system, is proposed to achieve satisfactory control performance even under the effect of persistent ship roll and heave perturbations. Specifically, a discrete model is first obtained by some careful transformation and discretization on the unactuated dynamics equations, based on which a novel model predictive controller is constructed. To reduce the complexity of the system, the unactuated states and the accelerations of actuated states are considered as system states and control inputs respectively. After that, the requirements for the payload position accuracy and swing suppression as well as other system constraints, are taken into full account by converting them into input constraints to facilitate subsequent handling. At last, hardware experiments are implemented on a self-built testbed, with the obtained results clearly illustrating the effectiveness and robustness of the proposed method. • Proposing a model predictive control method for the three-dimensional offshore boom crane disturbed by ship motions. • Regarding only unactuated states as system states, with the unactuated dynamics model considered to develop controller. • Introducing various practical constraints to guarantee convenient applications. • Experiment results present satisfactory control performance. [ABSTRACT FROM AUTHOR]

Abstract Overhead cranes, which have been extensively studied, are mostly simplified as single pendulums. However, in practice, the existence of lifting hook usually makes the crane present double-pendulum swing, i.e., hook swing with respect to the trolley and payload swing with respect to the hook. Therefore, a severe gap between theory and practice is generated. Due to this fact, lots of researchers are now working on the automation for double-pendulum overhead cranes (DPOC). However, with an extra unactuated degree of freedom, the control of DPOC is much more challenging than that of the simplified system due to its complicated dynamics, which is made even worse when considering payload hoisting/lowering and uncertain system parameters. To solve this problem, an enhanced-coupling adaptive controller is proposed for DPOC in this paper. Specifically, the payload hoisting/lowering motion is elaborately considered. Moreover, to improve the swing suppression performance, more swing information are incorporated into the construction of control inputs. Particularly, the uncertain payload mass is online estimated by a new adaption law ensuring precise identification, which further enhances the robustness of the proposed method. By utilizing Lyapunov techniques and LaSalle's Invariance Theorem, the closed-loop system is proven to be asymptotically stable around the desired equilibria. Finally, convincing hardware experimental results are presented to demonstrate the efficiency and superior control performance of the proposed method. [ABSTRACT FROM AUTHOR]

Researches on overhead cranes are mainly aimed at single-rope ones. However, to guarantee safety and increase the transportation efficiency, many overhead cranes tend to use more ropes to suspend the payload during the actual operation, which results in severe gap between theory and practice. The study on multi-rope cranes has achieved little progress in the past years, and no precise model has been proposed for such systems. So far, existing results for multi-rope cranes are usually based on linearizations or approximations, whose performance can be hardly guaranteed in many cases. Motivated to solve this problem, a dynamic model for multi-rope cranes is first set up in this paper by utilizing Lagrangian modeling method. Based on that, a nonlinear controller is further proposed, which incorporates more swing-related information into the control law, so as to enhance the robustness and swing suppression performance of the closed-loop system. The asymptotic stability of the desired equilibrium point is rigorously guaranteed by Lyapunov techniques and LaSalle’s invariance theorem. Finally, to demonstrate the feasibility and efficiency of the designed controller, hardware experimental results are provided as a convincing validation. [ABSTRACT FROM AUTHOR]

The past decades have seen increasing demands for the transportation capability of overhead cranes. In practice, as the cargoes grow larger and heavier, they often need to be cooperatively delivered by two cranes. However, even with wide application, the study of such dual overhead crane system (DOCS) still fails to receive enough attention from researchers. Until now, few results have been published for DOCS, and most of them present no rigorous analysis for the overall system dynamics. Motivated to solve these issues, a precise model is first set up for DOCS by utilizing the Lagrangian modeling method. Based on that, a nonlinear control strategy is further proposed, which elaborately considers the coordination between cranes. By utilizing Lyapunov techniques and LaSalle’s invariance theorem, the desired equilibrium point of the closed-loop system is proved to be asymptotically stable, without tuning to linearization or approximations. Finally, feasibility and efficiency of the proposed method are further validated by convincing hardware experimental results. [ABSTRACT FROM AUTHOR]

Aerial transportation is enjoying more and more popularity among disaster-oriented tasks, such as fire fighting, delivery of relief supplies, life-saving aid, and so on, which are infeasible for ground robots especially in complex terrains. However, the contradictory between transportation efficiency and payload swing suppression is an annoying problem, which badly limits the application of aerial transportation. In this paper, a time-optimal motion planning (TOMP) scheme is proposed with effective payload antiswing performance. To the best of our knowledge, it is the first minimum-time trajectory planning method designed for unmanned quadrotor transportation systems. Compared with existing methods, the proposed approach presents significant superiority in the sense that both nonlinear dynamics of the system and various constraints are taken into full consideration simultaneously. Specifically, the nonlinear system model is established using Lagrangian mechanics, based on which the augmented system is transformed into a nonlinear affine system regarding the acceleration/jerk as the control input without the need of the linearized system model. After discretization and approximation process, the time-optimal motion planning problem is converted into a standard nonlinear programming problem, wherein various practical constraints are considered, including the bounds of the payload swing, the quadrotor velocity, acceleration, and even jerk. Finally, the nonlinear programming problem is solved by the sequential quadratic programming (SQP) method. Experimental results are exhibited to illustrate the effectiveness and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

With the rapid development of nano-science, an atomic force microscopy (AFM) has been playing an increasingly important role in many fields. Nevertheless, hysteresis nonlinearity of a piezoelectric scanner affects the positioning accuracy and then the imaging performance of an AFM system, besides, the low data utilization rate of a traditional AFM tremendously limits the performance of the system. In this paper, Back Propagation Neural Networks (BPNN) is first used to model and compensate for hysteresis nonlinearity, afterwards, a Kalman filtering based method is proposed to replace the traditional data processing mode to improve system efficiency and image quality. To be specific, consider the hysteresis effect of a piezoelectric scanner, a two hidden layers BPNN is utilized for hysteresis modeling. Subsequently, a method based on cubic spline interpolation is proposed to compensate for hysteresis behavior. After that, to fully utilize the data of current scanning point and its adjacent points, the least square method is used to match sample height information in forward and backward scanning processes. Finally, for each scanning point, Kalman filtering is applied to process all the data with weighting factors recursively to acquire an optimal outcome, which yields more accurate height information than existing methods utilizing only forward scanning data. Experimental results are collected to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Overhead cranes, which are known as typical underactuated systems, are extensively studied in recent years. However, many existing methods for overhead cranes are developed based on a relatively ideal model, with little consideration for persistent external disturbances, unmodeled dynamics, uncertain system parameters and so on. Thus, their performance may badly degrade when it comes to practical applications, and this problem is made even worse when the system suffers from unmatched disturbances. To tackle this issue, a sliding mode control (SMC) method is proposed in this paper, which ensures satisfactory control performance even when the crane works under unfavorable conditions. The asymptotic stability of the desired equilibrium point is backed up with rigorous Lyapunov-based analysis. Furthermore, to alleviate the chattering problem inherent to SMC, a disturbance observer is further designed for overhead cranes to estimate and then eliminate the influence of the disturbances. Both simulation and hardware experimental results are provided to validate the efficiency and robustness of the proposed control strategy. To the best of our knowledge, this paper proposes the first method that can solve the control problem of overhead cranes suffering from both matched and unmatched disturbances, without tuning to linearizations or approximations. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient nonlinear controller for an offshore boom crane, which is a combination of a learning strategy and an adaptive robust control method. An offshore boom crane is a kind of typical underactuated system which has less number of actuators than its degrees of freedom (DOFs), and it is also a sophisticated nonlinear system with strong-coupling characteristics, therefore, controller design for this kind of system becomes an extremely challenging task. Moreover, different from an overhead crane fixed on land, an offshore boom crane fixed on a vessel suffers from some peculiar disturbances of the attached ship’s multi-dimensional movement induced by waves and ocean currents, which implies that the motion of the ship can cause a tremendous effect on this system. Considering the periodic property of sea waves, this paper proposes an adaptive repetitive learning control strategy containing a learning law to deal with the aforementioned practical problems, as well as an adaptive law to handle the unknown system parameters. Specifically, different from traditional learning control strategy, the proposed control algorithm successfully addresses unknown periods of the periodic disturbances by introducing a period identifier into the control scheme. Meanwhile, the developed control strategy presents good robustness against ever-lasting disturbances and unknown parameters. The stability of the designed closed-loop system is guaranteed in the Lyapunov sense. Furthermore, some comparative experimental results are presented to demonstrate the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper proposes a new nonlinear tracking control scheme with simultaneous unknown mass identification for magnetic suspension systems. Specifically, an amplitude-saturated adaptive control law is developed to achieve stable tracking and accurately estimate the unknown suspended mass simultaneously. The stability is assured with rigorous Lyapunov-based analysis. As far as we know, this is the first continuous control method for magnetic suspension systems with unknown levitated ball mass and actuator saturation, yielding an asymptotic result to achieve simultaneous tracking control and mass identification. Through hardware experiments, we verify the performance of the proposed method and compare it with existing methods. [ABSTRACT FROM AUTHOR]

For a piezoelectric tube scanner (PTS), this paper proposes an improved direct inverse tracking control algorithm and apply it to an atomic force microscope (AFM) to accomplish high-speed scanning tasks. That is, to enhance the high-speed tracking control performance of a PTS, an improved direct inverse rate-dependent Prandtl–Ishlinskii (P–I) model is firstly constructed, which includes a polynomial module to eliminate the structure nonlinearity. Based on the model, a practical feedforward control law is then designed to implement high-speed tracking control for a high-frequency trajectory with strong robustness, which presents the advantages of high-speed response, simple structure and convenient implementation. Subsequently, the designed feedforward law is combined with a feedback component, and the combined control strategy is employed in an AFM to accomplish fast imaging tasks. Numerous experimental results are then collected, which convincingly demonstrate the superior performance of the proposed practical model/control scheme. [ABSTRACT FROM AUTHOR]

A bridge crane is a complicated nonlinear underactuated mechatronic system, for which high-speed positioning and anti-swing control is the kernel objective. Existing methods for varying cable length cranes require either linearizations or approximations, when performing analysis, and they usually assume small load swing; moreover, the ranges of the tracking errors cannot be guaranteed during the overall process. Motivated by these facts, we present a new tracking scheme for cranes with load horizontal transportation and lowering control, which achieves simultaneous load swing suppression and elimination. To the best of our knowledge, the proposed method yields the first feedback closed-loop control result not needing linearization or approximation operations to the original nonlinear crane dynamics with cable length variation, while relaxing the common assumption imposed on load swing associated with existing methods. It can also guarantee that the tracking errors are always within a priori set bounds and converge to zero rapidly. Lyapunov-like analysis is implemented to support the theoretical derivations. We carry out hardware experiments to illustrate the superior control performance of the new method. [ABSTRACT FROM AUTHOR]

Highlights: [•] We obtain the first model-free OFB controller for the control problem of cranes. [•] The proposed approach does not need velocity signal feedback. [•] The controller is robust with respect to uncertainties/disturbances. [•] A novel energy exchanging and dropping mechanism is used. [•] Experimental results are included to validate the effectiveness and feasibility. [Copyright &y& Elsevier]

Abstract: We propose in the present paper an energy coupling-based output feedback (OFB) control scheme for 4 degrees-of-freedom (4-DOF) overhead cranes under control input constraints. Unlike existing crane control methods, the proposed approach can achieve superior control performance using only trolley-position/payload-swing feedback with saturated control inputs. In particular, a new concept regarding virtual payloads is introduced, together with a novel energy storage function, to successfully explore the characteristics of the crane dynamics. Based on that, an energy coupling OFB control law is proposed by taking the practical input constraints into account, which achieves both precise trolley positioning and efficient payload swing elimination. The corresponding stability analysis is guaranteed by Lyapunov techniques and LaSalle’s invariance theorem. Experimental results are presented to illustrate the superior control performance of the proposed scheme. [Copyright &y& Elsevier]

While presenting many advantages in application, the loss of actuators also brings about great challenge to the control of underactuated systems. Specifically, the unactuated DOFs (degrees of freedom) cannot be directly controlled, instead, they have to be stabilized indirectly through appropriate regulation of actuated ones. As an unfortunate fact, the natural couplings between actuated and unactuated states are often relatively weak. Hence, in many practical cases, even the actuated states are well regulated, the performance of the unactuated states still fails to meet expectations. Such phenomenon is particularly obvious when the system faces various disturbances, since the unactuated states are more vulnerable to them and more difficult to be re-stabilized. To deal with the aforementioned issues, a nonlinear gain-adapting controller is proposed for a class of underactuated mechanical systems, which incorporates extra unactuated states-related information into the controller to enhance the state couplings. In this way, the closed-loop system reacts more appropriately and efficiently to the unactuated dynamics, which is expected to obtain improved transient performance and robustness. With a relatively concise structure, the proposed strategy imposes few requirements on the system configuration, which makes it more convenient for practical application. Theoretical analysis and experiment results are presented to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

• A Gaussian–Hann kernel is designed for image filtering. • An adaptive image binarization method is proposed to implement object detection. • An advanced morphology prediction algorithm is proposed to correct image distortion. The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian–Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images. [ABSTRACT FROM AUTHOR]

Learning to perform air combat autonomously has been a long-standing challenge. The design of intelligent game strategies was difficult due to complex dynamic constraints and long decision-making process. Most existing approaches depend heavily on simplified aircraft models or complex hand-crafted rules. Different from previous works, this paper presents H3E, a novel and efficient Three-level Hierarchical decision framework embedding Expert knowledge, which gives birth to various strategies for high-fidelity one-on-one beyond-visual-range (BVR) air combat game. Inspired by the way pilots make decisions, we build a hierarchical framework to divide the air combat into several sub systems in which each level can perform its own task with much smaller exploration space. In addition, to make full use of the expert knowledge while minimizing its limitation, a novel "Rule-Imitation-Reinforcement" (RIR) training paradigm with an adjustable expert-guidance (AEG) loss function is established, which can increase the exploration efficiency as well as the game win rate. Finally, this work is evaluated in the Intelligent Air Game Simulator (IAGSim), a high-fidelity air combat simulation platform, through a series of games against the state-of-the-art (SOTA) methods. The learning process and game results verify the superior performance of our framework in terms of the exploration efficiency (higher rewards with the same training samples) and win rate (at least 72.9%) compared with the existing SOTA approaches. • A novel three-level hierarchical decision framework for air combat is proposed. • A 'Rule-Imitation-Reinforcement' training paradigm is newly introduced. • Making full use of expert knowledge via an adjustable expert-guidance loss. • Experiments show better exploration efficiency and game performance. • Interpretable strategies have emerged under the training framework. [ABSTRACT FROM AUTHOR]

With outstanding flexibility, pneumatic artificial muscles (PAMs) have shown unique advantages in human–robot interaction environments. However, asymmetric hysteresis nonlinearities, unknown transient or persistent time-varying disturbances, etc., seriously degrade control performance/robustness of PAM arms, and increase actuation failure risks. Meanwhile, existing disturbance rejection methods either rarely handle matched/unmatched disturbances, or require known disturbance dynamics. These defects limit the disturbance rejection ability in practical applications. To this end, a new adaptive output feedback control method is proposed in this article, which achieves efficient positioning and tracking control of PAM arms. Without requiring a priori knowledge of inverse plant models and disturbance upper bounds, the proposed method further enhances robustness against uncertainties and disturbances. Specifically, a generalized Bouc–Wen hysteresis loop is used to describe the asymmetric dynamic force of PAMs. Also, a new adaptive law is designed to compensate for the linearized parameters online. In particular, by applying a modified state observer to derive the equivalent-input-disturbance estimation solution, this article firstly realizes state observation, parameter estimation, and unknown transient/persistent external disturbance rejection simultaneously. The rigorous stability analysis demonstrates that the tracking errors converge to small neighborhoods of the origins. Finally, hardware experimental results with comparisons validate the effectiveness and robustness superiority of the proposed method. • Adaptive motion control with hysteresis compensation of nonlinear PAM arms. • Equivalent input disturbance design to handle matched and unmatched disturbances. • A new adaptive law for linearized hysteresis parameters with error excitation terms. • Convergence analysis for the adaptive and tracking errors at the equilibrium points. • Hardware experiments with comparisons to verify control performance and robustness. [ABSTRACT FROM AUTHOR]

Offshore crane systems are widely employed to transport cargoes on vessels due to their outstanding flexibility. However, they always suffer from various disturbances in the special working environment, which will appear in not only the actuated part (jib and cable) but also the unactuated part (cargo). In particular, some platform parameters cannot be measured precisely. Additionally, the task scenarios of offshore cranes are divided into stabilizing cargoes with respect to the ground and the ship deck, respectively, i.e., in the earth-fixed (inertia) and ship-fixed (non-inertia) coordinate frame. However, existing methods focus on the former. To address the above problems, a nonlinear observer-based adaptive fuzzy controller is suggested to stabilize the cargo with respect to the ship deck , and the swing can be simultaneously suppressed. The closed-loop stability is theoretically guaranteed by Lyapunov methods. To our best knowledge, this is the first control scheme that can stabilize the cargo with respect to the ship deck for underactuated offshore cranes with uncertainties, input dead zones, and external disturbances. Finally, we employ some experiments to validate the effectiveness of the proposed control approach. • Adaptive fuzzy control for uncertain underactuated offshore cranes. • Stabilize cargoes with respect to ship decks. • High-order sliding-mode differentiator to handle external disturbances. • Rigorous proof of stability based on Lyapunov techniques. • Validity demonstration of hardware experimental for the designed controller. [ABSTRACT FROM AUTHOR]

• Exposure to venlafaxine impaired disrupts courtship behaviour in adult zebrafish. • Venlafaxine reduced the locomotor movement of zebrafish larvae. • Venlafaxine altered the expression of the dopaminergic system-related genes. • Altered dopamine was associated with courtship behaviour in adult zebrafish. Venlafaxine, a serotonin-noradrenaline reuptake inhibitor, is a widely used antidepressant drug routinely detected in aquatic environments. However, its potential impact on courtship behaviour in zebrafish is unknown. We tested the hypothesis that venlafaxine disrupts brain monoamine levels and molecular responses essential for courtship behaviour in zebrafish. Zebrafish (Danio rerio) were exposed to venlafaxine (1, 10, and 100 μg/L) for 20 days. We evaluated the molecular levels and neuronal basis of the effect of venlafaxine on courtship behaviour. Here, we show that venlafaxine inhibited courtship behaviour in zebrafish and increased the transcript levels of 5-ht1a and 5-ht2c while decreasing the transcript levels of genes involved in the dopaminergic system, including th1, th2, drd1b, and drd2b. Venlafaxine upregulated 5-HT levels and downregulated dopamine levels. Moreover, the subordinate fish from the venlafaxine-exposed group had significantly lower motor activity than the subordinate fish of the control group. Collectively, our results reveal that venlafaxine can disturb brain monoamine levels, affecting courtship behaviour in adult zebrafish. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to such advantages as large output forces, variable stiffness, and strong safety, flexible pneumatic artificial muscles (PAMs) have been widely used in important fields, such as medical rehabilitation training and military exoskeleton assist. However, their complex hysteresis, creep, input uncertainties (caused by air pressure thresholds and unidirectional saturations), and sensitivity to external noises, etc., lead to difficulties in accurate modeling, parameter identification, and nonlinear control of PAM robots. Aiming at these problems, this paper designs an adaptive command filtering control method based on neural networks, which realizes satisfactory tracking control of a dual-PAM arm robot. Specifically, by introducing a barrier term, the designed feedback controller actuates tracking errors to converge to the neighborhoods of zero, and always limits the tracking errors within the desired bounds. Furthermore, a Lyapunov function is chosen to prove the stability of the closed-loop system. Compared with most of existing methods, this paper gives the first continuous controller to simultaneously deal with unmodeled dynamics, parametric uncertainties, and multiple input constraints of PAM robots with only measurable outputs being required. In the case, differential noises can be effectively suppressed, which is pretty beneficial to the control of PAM robots that are driven by highly compressed air. Finally, the feasibility and robustness of the proposed method are validated by a series of hardware experiments on a self-built PAM humanoid arm robot testbed. • Adaptive neural network control for nonlinear PAM robots with input uncertainties. • Command filtering control to handle differential noises. • Ensured convergence without violating the desired constraints of tracking errors. • Rigorous proof of stability based on Lyapunov techniques. • Validity demonstration of hardware experimental results of designed controllers. [ABSTRACT FROM AUTHOR]

Due to the backlash hysteresis, robust and accurate tracking control of a hysteresis system is a challenging problem. We propose in this paper an active observer based robust control scheme to reject the influence of hysteresis. Firstly, the normal piecewise C–H model of hysteresis plant was modified into a continuous form so that an active observer could be applied on. Secondly, an observer was designed based on the extended set-membership filter (ESMF). This intends to take the advantages of ESMF on the bound-guaranteed estimation and the unknown but bounded (UBB) noise tolerance. Thirdly, a robust controller was constructed using both the magnitude and the bounds of the hysteresis uncertainty estimated by the observer. The stability of the control scheme was theoretically proved. Experiments were conducted on a robotic flexible ureteroscope test-bed. The performance of the modified model, the ESMF-based observer, and the robust control were extensively verified, and the improvements benefited from this proposed scheme were demonstrated. [ABSTRACT FROM AUTHOR]