In this paper, we proposed a new approach to real time implementation of monitoring and control of robot system for smart factory. The reliability of the proposed monitoring control system was illustrated by simulatio...
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ISBN:
(纸本)9798331517939;9788993215380
In this paper, we proposed a new approach to real time implementation of monitoring and control of robot system for smart factory. The reliability of the proposed monitoring control system was illustrated by simulation and experiments for dual arm robot with eight joints.
In this paper, a novel approach is proposed for learning robot control in contact-rich tasks such as wiping, by developing Diffusion Contact Model (DCM). Previous methods of learning such tasks relied on impedance con...
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ISBN:
(纸本)9798350377712;9798350377705
In this paper, a novel approach is proposed for learning robot control in contact-rich tasks such as wiping, by developing Diffusion Contact Model (DCM). Previous methods of learning such tasks relied on impedance control with time-varying stiffness tuning by performing Bayesian optimization by trial-and-error with robots. The proposed approach aims to reduce the cost of robot operation by predicting the robot contact trajectories from the variable stiffness inputs and using neural models. However, contact dynamics are inherently highly nonlinear, and their simulation requires iterative computations such as convex optimization. Moreover, approximating such computations by using finite-layer neural models is difficult. To overcome these limitations, the proposed DCM used the denoising diffusion models that could simulate the complex dynamics via iterative computations, thus improving the prediction accuracy. Stiffness tuning experiments conducted in simulated and real environments showed that the DCM achieved comparable performance to a conventional robot-based optimization method while reducing the number of robot trials.
We introduce a novel hybrid vision/force control strategy for robotic devices designed to obtain clear and consistent images using probe-based confocal laser endomicroscopy (pCLE). Due to the variable nature of tissue...
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ISBN:
(纸本)9798350377712;9798350377705
We introduce a novel hybrid vision/force control strategy for robotic devices designed to obtain clear and consistent images using probe-based confocal laser endomicroscopy (pCLE). Due to the variable nature of tissue characteristics encountered during pCLE imaging, the conventional approach of pre-setting forces or focus metrics for either force or vision control is often impractical and inadequate. To address this, our strategy employs a blur metric called the CR score to assess the level of blur in pCLE images, enabling the attainment of clear and focused images. At the onset of a pCLE scan, the system autonomously determines the target CR score for vision control, in tandem with a real-time peak detection algorithm. Concurrently, force control is applied judiciously to prevent excessive force on the tissue, adjusting to ensure minimal force is applied, thus preserving image focus. This innovative approach facilitates seamless transitions between vision and force control, depending on the imaging conditions, thereby ensuring the acquisition of consistent pCLE images with minimal force. Our method marks a notable improvement over conventional PID force control techniques. By dynamically adjusting target forces and minimizing force application during operation, we not only enhance the precision and quality of pCLE imaging but also eliminate the dependency on manual pre-settings.
In this paper, considering overall deformations of the exoskeleton, we couple deformations relationship network (DRN) with fractional order viscoelastic (FOV) controller, proposing a novel DRN-FOV closed-loop control ...
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ISBN:
(纸本)9798350377712;9798350377705
In this paper, considering overall deformations of the exoskeleton, we couple deformations relationship network (DRN) with fractional order viscoelastic (FOV) controller, proposing a novel DRN-FOV closed-loop control method, endowing exoskeleton with stable dynamic walking ability. Simply by utilizing only the data from the 6-axis force/torque sensors, the DRN can directly capture the mapping relationship between the foot reaction force/torque of the exoskeleton and its overall deformations. We introduce the FOV to eliminate disturbances and stabilize during walking tasks. The closed-loop control method directly compensates for the overall deformations of the exoskeleton and enables the wearer to walk stably wearing the exoskeleton. To assess the effectiveness of the proposed control method, walking tasks were effectively carried out on subjects with varying body parameters using the developed exoskeleton. The experimental results show that the DRN-FOV closed-loop control method accurately estimates and compensates for deformations, resulting in an improved dynamic walking ability of the exoskeleton with wearers.
While the affine T-S fuzzy model offers additional accuracy in terms of describing system dynamics compared to the regular T-S fuzzy model, the problem is that the resulting LMIs for the controller design are structur...
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ISBN:
(纸本)9781665499248
While the affine T-S fuzzy model offers additional accuracy in terms of describing system dynamics compared to the regular T-S fuzzy model, the problem is that the resulting LMIs for the controller design are structurally infeasible without introducing additional inequalities. In this paper, first, we partition the state space into some cells and use the properties of the cells away from the origin to obtain favourable quadratic inequalities. Then two fuzzy controllers are given based on the partition. In addition, uncertainty in the system is also considered in the affine T-S fuzzy model. Finally, simulation results are provided for the verification of the proposed controllers.
Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynami...
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ISBN:
(纸本)9798350377712;9798350377705
Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynamic model that accurately describes the complex interactions between UAVs and both stationary and oscillating platforms. Integrating this model with a feedforward controller effectively compensates for GE, resulting in improved landing performance. The proposed GE model elucidates the relationship between GE and factors such as UAVs' velocity, throttle magnitude, and the motion of the landing platform. It highlights that the GE experienced during the landing process of UAVs is not only contingent on the current state but also related to past states. The resulting GE model is parsimonious and suitable for onboard computers with limited computational power, and its accuracy has been confirmed through a series of flight experiments. To demonstrate the effectiveness of the developed UAVs landing scheme, we compared our approach with robust control and internal model control methods. Experimental results indicate that the proposed landing strategy achieves faster and smoother landings, with at least a 22% improvement in smoothness and a 25% reduction in landing time.
X-ray security check is a prevalent placement measure that plays a vital role in ensuring public safety. However, detecting prohibited items poses a complex challenge due to their variable sizes within X-ray images. I...
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This paper proposes a novel integral reinforcement learning (IRL) based DC-link voltage control method for three-phase AC/DC converter. The proposed IRL control autonomously updates the optimal control gains using onl...
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Aggressive maneuvering is crucial for aerial vehicles to execute adversarial and penetration missions. However, this challenges the accurate tracking control of drones due to uncertainties induced by high-speed flight...
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ISBN:
(纸本)9798350377712;9798350377705
Aggressive maneuvering is crucial for aerial vehicles to execute adversarial and penetration missions. However, this challenges the accurate tracking control of drones due to uncertainties induced by high-speed flight. Therefore, firstly, a highly dynamic tracking control framework is proposed to actualize the accurate tracking of aggressive trajectories with velocities up to 15 m/s (i.e., 54 km/h) and acceleration of 2 g. Secondly, in order to mitigate the impact of conjoint effects on uncertainty estimation during aggressive flights and to ensure that uncertainty is smoothly compensated, a novel adaptive non-linear extended state observer (ANESO) with noise suppression and peak attenuation capabilities is designed. Finally, extensive comparative simulation and real-world practical experimental results certify the superiority of the proposed control strategy in tracking aggressive trajectories.
This paper introduces a position control system for a motion stage driven by a low-energy C-core reluctance actuator. The central concept explored here is the utilization of a variable air gap to enable energy-efficie...
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ISBN:
(纸本)9798350377712;9798350377705
This paper introduces a position control system for a motion stage driven by a low-energy C-core reluctance actuator. The central concept explored here is the utilization of a variable air gap to enable energy-efficient operation of the motion stage. First, we show the design and mathematical model of the reluctance-actuated motion system (RAMS). Then, by analyzing open-loop responses of the RAMS under various conditions including variable air gaps and different excitation voltages, we show that using variable air gap can reduce the required current. Finally, the paper formulates a control approach that combines a feedforward controller to linearize the RAMS's dynamic behavior and a state feedback controller to achieve tracking performance. Experimental results demonstrate the effectiveness of this control approach in achieving tracking objectives with errors that are less than 2% for constant desired displacement and less than 10% for tracking a sinusoidal reference signal.
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