Fast motion and low vibration are often conflicting requirements in industrial robots. Due to joint flexibility, operating at high accelerations can excite mechanical resonance, leading to vibrations that may accelerate gearbox wear and degrade control performance. Effectively suppressing these vibrations requires accurate modeling, identification, and compensation of joint flexibility across the entire workspace and under various payloads. The conventional two-mass model, which focuses on joint rotational stiffness in the gearbox, fails to capture the global flexibility characteristics, as flexibility also arises from bending in the bearings. This article presents a practical multi-local-mode-based model and the corresponding identification method to globally characterize a manipulator’s joint flexibility. Based on this model, a vibration suppression method is developed to mitigate mechanical resonance, achieving low vibration even at high acceleration. Comparative experiments demonstrate that the proposed approach effectively suppresses vibrations across the workspace and under various payload conditions.

Target enclosing control of uncrewed surface vehicles (USVs) is essential for maritime monitoring and security. For noncooperative targets in practical scenarios, relying on global information becomes ineffective, thus making vision-based perception a promising solution. However, achieving reliable vision-based enclosing faces significant challenges including field of view (FOV) constraint and various uncertainties. To address these challenges, this article presents a monocular vision-based target enclosing framework for USVs. The proposed framework first incorporates a tailored YOLOv5-based detector coupled with a bearing-only state estimator, which can achieve reliable target state estimation from monocular vision. Then, to maintain continuous visual contact, a control barrier function based safety-critical filter is developed to constrain the USV’s FOV during target enclosing. Furthermore, a dual-layer extended state observer (ESO) based control structure is implemented to handle various uncertainties, where kinematic-level ESOs compensate for unknown USV lateral velocity and target motion, while dynamic-level ESOs address lumped dynamic uncertainties. This approach ensures robust and accurate target enclosing with continuous visual tracking. Both simulations and experiment are implemented to verify the effectiveness of the proposed framework.

We study the problem of fairly allocating a set of indivisible items among heterogeneous agents with a set of weights quantifying their distinct entitlements for different types of items. In this setting, each agent is associated with a preference (utility) function over the items according to its different task processing abilities. To measure the fairness, we explore the multi-weight version of MMS called multi-weight maximin share (MWMMS). In spite of the relatively negative result shown by the counterexample for MWMMS, we present an algorithm guaranteeing that each agent obtains a bundle worth at least$1/n$of its MWMMS for the restricted setting. Finally, based on the fact that the relatively bad approximation occurs only in the polar case, we provide a stochastic variant, where the weights are randomly sampled from the given distribution. Theoretical analysis shows that the$1/n$-approximation can be guaranteed with a desirable probability.

The Multi-Agent Combinatorial Path Finding (MCPF) problem is a generalized version of the Multi-Agent Path Finding (MAPF) problem, in which each agent must collectively visit multiple intermediate target locations on the way to its final destination. The state-of-the-art approach for addressing MCPF, known as Conflict-Based Steiner Search (CBSS) [1], leverages K-best joint sequences to create multiple search trees, and employs a CBS-like search to resolve collisions for each tree. Despite its optimality guarantee, CBSS is computationally burdensome due to the duplicated collision resolutions across multiple trees and the computation of the K best joint sequences. To address these challenges, we propose a novel algorithm called Improved Conflict-Based Steiner Search (ICBSS), aiming at expediting CBSS by replacing the multi trees with a single constraint tree (CT), which can be implemented by interleaving the time-dependent traveling salesman algorithm to compute the optimal joint path for agents under the newly generated constraints in each CT vertex. Additionally, we introduce a sub-optimal variant of ICBSS, which improves computational efficiency at the expense of solution optimality. Empirical results show that ICBSS outperforms state-of-the-art MCPF algorithms on a variety of MAPF instances.

Autonomous berthing remains a key challenge for unmanned surface vehicles (USVs), especially in dynamic marine environments where traditional methods relying on GPS, wireless communication, or visual markers face limitations due to signal attenuation and lighting changes. This article proposes a vision-based robust berthing pose estimation framework that integrates infrared light arrays and ArUco fiducial markers, enabling accurate and real-time pose estimation. The YOLO-v5n berth object detection model has been fine-tuned on custom datasets for berthing scenarios, while TensorRT optimization ensures efficient deployment on embedded platforms. The system utilizes adaptive image processing techniques, including Otsu binarization, Canny edge detection, and centroid-based light center localization, to isolate infrared LEDs and extract their geometric features under different illumination conditions. The perspective n-point (PnP) algorithm was used to estimate USV’s relative pose to the light array. The framework has been validated through experiments under different conditions, providing a reliable solution and potential applications for USV autonomous berthing.

Digital simulation of the full operation of a remotely operated vehicle (ROV) is an economically feasible way for algorithm pretesting and operator training prior to the actual underwater tasks, due to the huge difficulties encountered during the underwater test, high equipment cost, and the time-consuming nature of the process. In this paper, a human-interactive digital simulation platform is established for the navigation, motion, and teleoperated manipulation of work-class ROVs, and provides the human operator with the visualized full operation process. Specially, two mechanisms are presented in this platform: one provides the virtual simulation platform for operator training; the other provides real-time visual and force feedback when implementing the actual tasks. Moreover, an open data interface is designed for researchers for pretesting various algorithms before implementing the actual underwater tasks. Additionally, typical underwater scenarios of the ROV, including underwater sediment sampling and pipeline docking tasks, are selected as the case studies for hydrodynamics-based simulation. Human operator can operate the manipulator installed on the ROV via the master manipulator with the visual and force feedback after the ROV is navigated to the desired position. During the full operation, the dynamic windows approach (DWA)-based local navigation algorithm, sliding mode control (SMC) controller, and the teleoperation control framework are implemented to show the effectiveness of the designed platform. Finally, a user study on the ROV operation mode is carried out, and several metrics are designed to evaluate the superiority and accuracy of the digital simulation platform for immersive underwater teleoperation.

With the expansion of application range of quadrotors, high-performance safety flight is getting more attention, where the health of actuators is critical. However, based on the commonly used loss of efficiency models, such fault-tolerant control methods are limited in performance to deal with different types of actuator faults in targeted ways. In this paper, by fully utilizing the more detailed and accurate models, the proposed adaptive robust fault-tolerant control has strong fault tolerance ability while maintaining excellent trajectory tracking accuracy. Firstly, the actuator model is established including motor dynamics and propeller model, which can reflect different types of actuator faults to the changes in different physical parameters instead of single-type efficiency factors. Additionally, aerodynamic drag is explicitly considered in quadrotor dynamics for improving control accuracy. Then, adaptive robust control is developed on these bases with comprehensive adaptation mechanism. To be specific, actuator parameters are actively estimated by recursive least square, so that actuator faults can be compensated directly in a targeted way without fault diagnosis. Subsequently, aerodynamic drag is effectively compensated through gradient-type adaptation, while the remaining uncompensated uncertainties are further suppressed by robust feedback. Finally, the comparative experiments demonstrate that the proposed method achieves much higher control accuracy than other compared methods, and it can maintain the same level of accuracy in faulty and fault-free case. Upon sudden faults, the proposed method exhibits the fastest response speed with minimal positional overshoot. Note to Practitioners—This paper aims to improve the flight safety of quadrotors while maintaining good trajectory tracking accuracy. Considering the most critical factor, i.e., actuator faults, it is usually modeled as loss of efficiency in most of literature on fault-tolerant control. However, the causes of faults are diverse, and it is not possible to effectively compensate for all kinds of faults through single-type efficiency coefficients. In fact, the occurrence of faults is associated with a change in a certain parameter of the system. Based on this, by fully utilizing the dynamics model of quadrotor and actuator, the effective online adaptive estimation algorithm is designed specifically for key actuator parameters related to faults. Therefore, more targeted compensation can be achieved for actuator faults caused by different reasons. In addition, those parameters that cannot be identified in advance but has great impact on accuracy, such as aerodynamic drag coefficients, are also explicitly considered and adaptively compensated. As for the remaining uncompensated uncertainties including external disturbances, robust feedback is introduced to ensure stability against them. The experimental results indicate that, for different types of actuator faults, the proposed method can achieve excellent trajectory tracking accuracy comparable to the fault-free cases. The proposed method can also be applied to other kinds of autonomous vehicles, such as underwater vehicles and aerospace vehicles.

Independent metering systems (IMSs) have shown superior performance in hydraulic industrial applications because of the high power density, large force output, and high control freedom. However, the presence of mechanical safety structures, such as relief valves and replenishing valves, introduces complex constraints that significantly limit performance improvements of the IMS. If the constraints are violated, then some undesirable phenomena, such as cavitation, overflow, and pressure surge, will occur to make the system lose accuracy and stability. In this article, a double-loop control strategy, which combines theouter loop constrained trajectory planner and the inner loop adaptive robust motion controller (ARC), is developed to realize the constrained motion control of the IMS. In the outer loop, both kinematic and dynamic constraints are transformed into time-varying constraints on the replanned trajectory, which are calculated based on the state feedback online to optimize the planner's performance. A time-optimal motion trajectory is planned using a third-order nonlinear filter, ensuring convergence to the original reference while meeting the assigned constraints. In the inner loop, the high-performance ARC controller is designed to make the IMS track the replanned trajectory despite uncertainties and nonlinearities. To demonstrate the constrained performance of the proposed double-loop control framework, experiments with different control strategies are conducted on an IMS test bench.

Accurate rigid-body dynamics is crucial for serial industrial robot applications such as force control and physical human-robot interaction. Despite decades of research, the precise identification of dynamic parameters—particularly low-magnitude inertia parameters—remains a challenge for serial industrial robots. Researchers usually focus on developing various parameter estimation methods, while optimizing exciting trajectories in similar ways, typically minimizing the condition number of the information matrix. However, such optimization usually fails to ensure sufficient excitation for each parameter, due to non-convex coupling effects. To address this limitation, we propose a fully decoupled rigid-body dynamics identification (FDRDI) method in this article. This approach innovatively eliminates coupling effects by using novel symmetrical exciting trajectories based on reciprocating S-curve (RSC). This innovation enables the independent identification of dynamic parameters associated with joint friction, as well as the gravity and inertia of links and payloads. Comparative experiments show that FDRDI achieves superior identification accuracy, evidenced by reduced joint torque prediction errors and payload parameter estimation errors.

Twisted and Coiled Actuators (TCAs) are promising in soft robotics for their high energy density, light weight, and low voltage. However, current TCA-based soft robots face challenges of limited deformation and control precision, mainly due to the preloading requirement of TCAs and the lack of suitable intrinsic sensing capabilities. To address these issues, we designed a TCA with high load capacity, free stroke, and self-sensing capabilities, proposed flexible optical fiber-based posture and tactile sensing methods, and developed a multi-loop feedback controller. Collectively, these enable millimeter-level tracking accuracy in a soft tentacle robot. The TCA, with an optimized manufacturing process, achieves a 30% free stroke without preloading, a 32% improvement in ultimate stress, and temperature self-sensing capabilities with a maximum error of less than 6%. Combining the TCAs with compliant macro-bend optical fibers and soft optical waveguides, we created a soft robotic tentacle with intrinsic posture and tactile sensing, and designed a multi-input multi-output closed-loop and feedforward controller. Experiments demonstrate that the model-based feedforward significantly improves the control performance, reducing the rise time by 15.3%. The trajectory tracking error remains within the millimeter range, and the repetitive positioning error for hexagonal trajectories reaches sub-millimeter precision. The tactile sensor of the robot enables real-time perception of the object's modulus and pressing states. These findings highlight the soft robotic tentacle's potential for various applications, including underwater exploration, detection, and sampling.

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances.

It is well known that a nonlinear system can be represented in a linear lifted feature space according to the Koopman operator theory. However, the approximation errors are always ignored, which may damage the control performance. Therefore, this paper presents a deep Koopman-based two loop control structure, where nonlinearities, uncertainties, and constraints can be handled simultaneously. Namely, a deep Koopman linear model is trained off-line to approximate the dynamic of nonlinear system. Accordingly, an optimization problem is introduced in the outer loop to replan the desired trajectory such that state and input constraints can be satisfied. Considering the model uncertainties introduced by the Koopman linear model, an adaptive robust controller is synthesized in the inner loop to ensure that the optimization result of the outer loop can be strictly tracked. In this way, fast transient response can be reached by the outer loop and the high motion tracking accuracy can be promised by the inner loop. The proposed framework’s advantages and efficacy are evidenced through comparative simulations conducted on a 2-DoF robotic manipulator.

The safety of quadrotor has received increasing attention, and fault-tolerant control for complete actuator failures presents significant challenges. An adaptive robust fault-tolerant control framework is proposed in this paper, capable of uniformly handling situations from fault-free case to complete actuator failures, without controller switching. Actuator faults are actively compensated by two adaptive methods: firstly, direct adaptation is designed for the control input efficiency coefficients; and secondly, the filtering-based disturbance estimation is integrated to address lumped disturbances. To avoid hard switching of controllers and achieve unified fault-tolerant control, the attitude control employs the primary-axis control method. This is combined with an optimization-based prioritized control allocation, which prioritizes the control of force direction while sacrificing yaw angle tracking in the event of severe actuator failures. Numerical simulation demonstrates that the proposed controller can actively tolerate unpredictable complete actuator failures. Moreover, the integration of two fault compensation methods enhances fault tolerance performance than one method alone.

The visual servoing control extends the potential application of cable-driven hyper-redundant manipulators (CDHRM) to automatically execute tasks by sensing the visual information of environment. However, the high proportion of feature point mismatching and visual occlusion may occur in the complex and narrow environment that CDHRM works in, which make the traditional image-based visual servoing (IBVS) system hard to work or even cause instability. In this article, a novel IBVS system is proposed for CDHRM with eye-in-hand configuration, where the ability of mismatching resistance is improved by optimizing the vector of feature points, while the stability and tracking performance are still guaranteed. The maximum likelihood estimation random sample consensus (MLESAC) algorithm is designed to estimate the inlier model that resists mismatching and extracts well-matched feature points (namely the inliers) for the first captured image. Since the inliers may become mismatched in the newly captured image, the inliers and inlier model are both updated according to the feature quality weights. As a result, the newly mismatched feature points are excluded so that the weighted feature vector with less mismatching and higher quality can be generated. Subsequently, the weighted IBVS control law based on this vector is designed to achieve the mismatching resistance as well as guarantee the asymptotic stability and tracking performance of system. Comparative experiments are implemented for the proposed MLESAC algorithm and novel IBVS system, and the results verify that our method has better adaptability to the environment when applied in CDHRM, even with partial visual occlusion.

Multiple degrees-of-freedom (multi-DOF) hydraulic manipulators are usually recognized as hard-to-control systems to achieve dynamic trajectory tracking because of strong nonlinearities, uncertainties, and complex couplings within the dynamics. In practice, when the end-effector of the multi-DOF hydraulic manipulator is tracking a given trajectory continuously, some joints may experience frequent stop-and-go or low-speed motions due to its kinematics. In such situations, dynamic friction becomes one of the main factors that affect the control performance. Inadequate compensation for the dynamic friction can result in undesired crawling or oscillatory behaviors of the manipulator. However, it is challenging to make effective compensation of the dynamic friction in control design due to its complicated and nonlinear properties. In this article, motion control of a multi-DOF hydraulic manipulator with extra consideration on the nonlinear friction for dynamic trajectory tracking is proposed. First, to make a more precise compensation of the nonlinear friction force, an improved LuGre model ensuring continuity and differentiability is developed, along with the method for obtaining nominal values of internal friction state based on desired trajectories. Then, a model-based adaptive robust motion controller is developed for the multi-DOF hydraulic manipulator. The nonlinearities and uncertainties of the high-order dynamics are well addressed in the closed-loop system, and the transient and asymptotic tracking performance can be guaranteed in theory. Finally, experimental validation was conducted, and the comparison with existing methods showed the improved tracking performance.

Gantry cranes are widely used for transporting containers in industry. Lacking in model tests and practical control strategy, the problem that the load suffers swaying due to disturbances such as wind and inertia still exists for spatial multi-rope gantry cranes. In this paper, a scale model of the complete hoisting system is designed and constructed based on a realistic multi-rope gantry crane prototype. Also, considering various constraints in practice such as the non-negative tension in ropes, dynamics of the hoisting system is analysed and a practical anti-sway control strategy is proposed which consists of a robust adaptive controller and a parallel redundant distribution algorithm of rope tension for decoupling. Comparative experiments on the scale model show that the proposed control strategy achieves significant anti-sway effect, and is feasible to be applied in industrial practice.

The motion control technology of electro-hydraulic actuators has great significance in industrial applications. Constraints significantly limit the motion control performance of actuator motion control, in addition to the inherent nonlinearities and uncertainties of the electro-hydraulic systems. The constraints comprise kinematic and dynamic constraints, and they can be time-varying owing to variations in the system. If the constraints are not fulfilled, the control accuracy may be adversely affected, for instance by actuator vibration, cavitation, or even instability. This article proposes a constrained motion control strategy for a variable-speed pump-driven hydraulic actuator. To robustly track a desired trajectory under constraints, the control strategy combines a nonlinear filter-type trajectory planning strategy and an adaptive robust motion controller. The trajectory planning strategy is designed by considering dynamic and kinematic constraints of the electro-hydraulic system, and it synthesizes a trajectory that reaches the given reference in minimum time while fulfilling these multiple constraints. Meanwhile, the adaptive robust motion controller tracks the synthesized trajectory with guaranteed control accuracy in the presence of inherent nonlinearities of the electro-hydraulic actuator. In addition, the assignments of the multiple constraints are adjusted in real time, which further optimize the constrained motion control performance. Comparative experiments with various given references were conducted to verify the advantages of the proposed constrained control strategy.

Teleoperation of multiple agents has the unique advantage to complete tasks with wide range and is an effective solution to help agents avoid obstacles with human intelligence, especially when encounters the local-minima problem. In this article, a novel nonlinear single-master–multislave (SMMS) teleoperation control framework is proposed for multiple mobile agents to achieve obstacles avoidance under delays, nonlinearities, various uncertainties, and nonholonomic constraints. Namely, the slave trajectory planner is designed to cope with the nonholonomic constraints caused by underactuated characteristics of slave agents, while the slave obstacle avoidance planner is designed to cope with obstacles in the environment, which can avoid the obstacles by artificial potential function (APF)-based obstacle avoidance algorithm. Particularly, considering that the APF usually encounters the local-minima problem, a leader selection algorithm is designed for the slave obstacle avoidance planner and a virtual force feedback is designed for the master subsystem, where the slave agents can get rid of local-minima points while teleoperated by human operator with confident force feedback. The global stability of the overall system can be guaranteed under the proposed radial basis function neural network (RBFNN)-based adaptive sliding mode master controller and slave formation controller under delays, nonlinearities and various uncertainties. The comparative experiment is implemented, and the results show the effectiveness of proposed control framework in the achievement of good performance including position tracking, force feedback, and formation and obstacles avoidance while the stability is guaranteed.

Motion control of mechatronic system with state and input constraints while achieving excellent integrated performance, such as robustness, high tracking accuracy, fast response, and slow overshoot, has always been a challenging issue. However, most existing relating studies merely focus on how to ensure stability under constraints, and few take integrated performance into account. In this article, we proposed a direct optimization based compensation adaptive robust control (ARC) approach, which is under a two-loop feedback structure, where the outer loop directly online replans both the model compensation term and the reference that conform to the constraints; and the conventional ARC control law is synthesized in inner loop to ensure guaranteed tracking accuracy when facing nonlinearity, parametric uncertainties, and external disturbances. Motion control of a linear motor was considered through this article as an introductory example. Comparative experiments are carried out and the results further verify the superiority and effectiveness of the proposed scheme.

Multilateral telerobotic system has potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback, and cooperative manipulation under time delays, various uncertainties, and external disturbance. In this paper, a novel radial basis function neural network (RBFNN) based adaptive sliding mode control design is proposed for nonlinear multilateral telerobotic system with n-master-n-slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (nonpower signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral telerobotic system with 2-master-2-slave manipulators is implemented. The results show the good performance of our design.

Four-wheel independently driven mobile robots are widely used in industrial automation, intelligent inspection, and outdoor exploration. The traditional kinematic control is usually applied for them, where only the chassis kinematics is taken into account and the robot dynamics (especially the wheel dynamics) is normally ignored. It may lead to some performance limitations such as the chattering phenomenon during robot rotating, because of the overactuation characteristic by four driving wheels. To address these problems, the integrated dynamic model is proposed, which includes chassis kinematics, chassis dynamics, wheel-ground interaction, and wheel dynamics. Subsequently, different from kinematic control, a model-based coordinated adaptive robust controller is developed, which generally consists of three-level designs for different parts of robot dynamics, and directly generates the motor driving torque commands for four wheels. The stability and tracking performance are theoretically guaranteed. Comparative experiments are carried out, and the results show the better performance of our proposed scheme.

Trajectory planning approaches including off-line and on-line algorithms are developed to deal with physical constraints in practical systems. However, by now the existing trajectory planning algorithms have to assume that the desired trajectory function to be planned is fully or at least partly known in advance, and it may not be true in some applications. To overcome this limitation, a general framework of on-line planning a desired trajectory under physical constraints whose function is unknown in advance is proposed. The desired trajectory is first on-line interpolated to achieve a mathematical expression, and then planned under the physical constraints by the bound estimator and nonlinear filter. A heuristic critical test curve algorithm is proposed to solve the potential stability issue. A telerobotic system, where the function of slave-desired trajectory is unknown in advance, is selected as a typical case. The experimental results validate the effectiveness of the proposed planning algorithm.

For dual-linear-motor-driven (DLMD) gantry systems widely used in industrial applications, the strong mechanical coupling usually makes it difficult to achieve both good tracking and smooth operation performance simultaneously. In most of the existing control schemes, only the pure motion synchronization is considered, which may produce large internal forces leading to performance degradation and control chattering/saturation. In this paper, an accurate MIMO mathematical model of a DLMD gantry including both the traditional linear motion and the previously ignored rotational motion around the mass center is given, leading to a better understanding of the mechanical coupling and the internal forces caused by the rotational dynamics. Additionally, some physical parameters in the rotational dynamics having significant influences on the synchronization performance are discussed (e.g., the actual centroid position essentially determines the proper thrusts assigned to two parallel motors). An advanced synchronization control scheme is presented subsequently by directly considering the additive rotational dynamics and accurate parameter estimation (e.g., the actual centroid position), which not only synchronizes the motions of two parallel motors but also regulates the internal forces. The technique of integrated desired compensation direct/indirect adaptive robust control is applied to synthesize the synchronization controller for both accurate parameter estimation and a guaranteed robust performance to various uncertainties. Comparative experiments with previous control schemes show the effectiveness and better synchronization performance of the proposed method.

A DC/DC boost converter exhibits highly nonlinear properties and subjects to certain uncertainties, like load change, input voltage variation, and parametric uncertainties. This paper first presents an improved accurate model of the converters, including the parasitic elements and model uncertainties, which are usually existed in actual systems but not (or not sufficiently) considered in most of the literatures. In view of the non-minimum phase nature of boost converter, a new indirect control scheme is proposed, in which the output voltage is indirectly controlled by tracking a corresponding inductor reference current. An integrated direct/indirect adaptive robust controller (DIARC) is presented to achieve both accurate parameter estimation and perfect current tracking in the presence of both parametric uncertainties and uncertain nonlinearities. With an accurate parameter estimation, the corresponding inductor reference current can be precisely calculated. A rigorous theoretical proving is given and simulation results shows the effectiveness of the proposed controller and control scheme.

Recently the research on hydraulically actuated exoskeleton becomes an attractive topic for those application requirements of human performance augmentation. The control goal of this type of exoskeleton system is to minimize the human machine interaction force. And it becomes more challenging for hydraulically actuated lower limb exoskeleton where the multi-variable nonlinear dynamics is quite complicated and multiple walking phases are existing. Furthermore, since the exoskeleton is driven by the hydraulic actuators, the accurate output force tracking can not be easily realized due to the large compressibility of hydraulic oil. This paper focuses on the human machine interaction force control and the walking phase partition of the hydraulically actuated lower limb exoskeleton. Firstly, a cascade interaction force control strategy is proposed for a 3-DOF support leg which is the basic partitioned module of the lower limb exoskeleton. The spring model is built for the dynamics of human-machine interface, and a high level controller minimizing the integral of human-machine interaction force is designed to generate the desired joint trajectories of the exoskeleton which can be considered as the human motion intent. Subsequently, an independent joint based PID controller is developed in the low level to achieve the good tracking of the above generated human motion intent. Secondly, the exoskeleton system in different walking phases is partitioned into three serial chain manipulator modules. For each serial chain manipulator module, the proposed cascade interaction force controller is applied to minimize the human machine interaction force at the end effector. Finally, the walking experiments with 20Kg load on a practical hydraulically actuated lower limb exoskeleton are carried out to validate the effectiveness of the proposed approach.

To realize the high performance potential of linear motor drive systems, various nonlinearities inherited to the system and their compensations have been extensively studied during the past decade. However, existing research tends to focus on one or several types of nonlinearities at a time and thus do not offer a complete overall solution. This paper studies precision motion control of linear motors in the presence of parameter variations and disturbances. An adaptive robust control (ARC) algorithm with simultaneous compensation of all significant nonlinearities is developed. Those nonlinearities include Coulomb friction, cogging force, and nonlinear electromagnetic field effect. The proposed ARC with and without nonlinearity compensation have also been implemented on theY-axis of a linear-motor-driven industrial gantry. Comparative experimental results show that the proposed ARC algorithm with simultaneous compensation of all significant nonlinearities achieves better motion tracking performance than existing ones. In addition, high-frequency structural flexible modes due to bearing, which are neglected in the previous researches, are explicitly identified experimentally, and their effects are carefully examined. Theoretical analysis is then conducted to generate a set of practically useful guidelines on the tuning of controller gains to optimize the achievable performance in practice.