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Item type:Publication, Access status: without files , Internal dissipation allocation in control Lyapunov function designThis letter studies how control Lyapunov function (CLF) structure affects CLF-based control under input constraints. In relaxed CLF-based quadratic programs (CLF–QPs), a mismatch between the prescribed dissipation and the CLF-induced input channel may cause excessive actuation demand, increased relaxation, and chattering-like behavior near the zero-channel set. A recursive backstepping representation exposes the internal dissipation and shows that uniformly increasing recursive dissipation strengthens zero-channel dissipation but also raises downstream actuation demand. To reshape this trade-off, a Lyapunov-level allocation mechanism redesigns the virtual controls, recursive coordinates, and dissipation profile. Simulations on a nonlinear spring–damper system illustrate the weak-channel effect and the dissipation–actuation trade-off, and show that the shaped CLF achieves convergence and disturbance attenuation comparable to high-gain uniform designs with lower control effort and a substantially smaller relaxation level.Publicationtype: Journal Article11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A cascaded strategy with embodied artificial intelligence: forward kinematics solutions for CCRobot-SThis paper presents a novel cable-climbing mechanism: the Collaborative Climbing Robot Squad (CCRobot-S), a variant of Reconfigurable Cable-Driven Parallel Robots (R-CDPR), specifically designed for the inspection and maintenance of stay cables. The forward kinematics of the CCRobot-S robotic system, however, is inherently mathematically intractable. This research proposes a novel cascaded strategy with Embodied Artificial Intelligence (EAI) to effectively tackle the forward kinematics problem. In this proposed strategy, a lightweight deep learning-based model integrated with numerical method optimization supplants traditional methods, providing feedback on the poses of the flying platform to the control loop of the CCRobot-S robotic system. It provides an approximate solution as initial values through a deep neural network by learning from physical or simulated interactive experiences of CCRobot-S, and then transfers the suitable initial values with kinematic constraints or physical constraints that are near the real solution to the numerical method. This process achieves a stable and robust solution for the forward kinematics of CCRobot-S. This article includes the foundational kinematic analysis of CCRobot-S, the formulation of the CCRobot-S model, a comprehensive introduction and analysis of the cascaded strategy, including the dataset preparation, the training configuration, the solution inference, and the numerical method optimization. Comprehensive evaluations and experiments were undertaken to examine the proposed strategy. The results reveal and confirm that the deep-learning neural network implemented in the CCRobot-S robotic system is effective. Additionally, the proposed cascaded strategy achieves higher prediction accuracy than the standalone neural network approach under the condition of real-time execution (position error reduced from (Formula presented.) mm to (Formula presented.) mm in the X direction, from (Formula presented.) mm to (Formula presented.) mm in the Y direction, and from (Formula presented.) to (Formula presented.) in the (Formula presented.) orientation). The cascaded strategy also guarantees convergence in 100 (Formula presented.) of test cases (50/50) and demonstrates enhanced stability and robustness (1:1 mapping from the joint space to the task space)relative to the conventional Newton-Raphson algorithm's numerical method. These attributes are crucial and necessary for the CCRobot-S system to be effectively deployed in real-world applications.Publicationtype: Journal Article37 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , CCRobot-S: A robotic cable-climbing squad collaborating for fast inspection and heavy-duty maintenanceThis study introduces a novel climbing strategy, reconfigurable parallel-type cable-driven climbing designed for long-span, large-scale bridge stay cable robotic applications, which has the potential to revolutionize the stay cable inspection and maintenance practice. The proposed methodology features the development of a Collaborative Climbing Robot Squad (CCRobot-S), which builds upon the design principles of the previous CCRobot series. In this study, CCRobot-S implements a parallel-type cable-driven manipulation design, allowing for reconfigurable kinematic morphology by its movable anchor bases and realizing the capacity of crossing over the stay cables for its flying platform. The collaborative robot squad design liberates the dimensions and scales of the robot's reachable workspace and moves the part of the robotic system that indeed needs to be moved, enhancing the working efficiency and climbing agility. This strategy also utilizes controllable adhesion instead of friction to interact with the bridge cable surface for the flying platform, realizing force multiplication for forceful manipulation. Toward bringing high efficiency and heavy-duty capacity, we propose the applicable climbing frameworks (zero-downtime climbing gait for cable inspection and spider-like climbing gait for cable maintenance) and the optimization frameworks (optimal anchor configuration for the movable anchor bases and optimal grasp arrangement for the flying gripper). This article includes the exploration of the design and climbing gaits of CCRobotS, the formulation of the CCRobot-S model, a comprehensive analysis of its workspace, and its climbing strategy and optimization. Extensive experiments have assessed the proposed climbing strategy's effectiveness and showcased CCRobot-S' capabilities.Publicationtype: Journal Article32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Developing a climbing robot for stay cable maintenance with security and rescue mechanismsThe significance of climbing robotic systems for cable maintenance is escalating in both academic research and real-world applications. As these systems are poised for real-world deployment, it is imperative to develop security and rescue mechanisms that ensure robots' intrinsic safety and robustness in dealing with uncertainty factors. This study presents a novel cable climbing robot designed with a climbing platform, a robotic manipulator integrated with specialized maintenance tools, and a gripper to withstand dynamic loads and impacts from maintenance operations. In addition, we propose the variable-damping safe-landing mechanism, the rescue mechanism, and the fusible gripper mechanism to counteract substantial disturbances in worst-case scenarios. Extensive experiments have been conducted to evaluate the proposed robot and its security and rescue mechanisms. The cable climbing robot has a heavy-duty capacity of 45 kg and an obstacle-negotiation ability of 10 mm. It also demonstrated its capabilities in various maintenance tasks, such as cable inspection, grinding, or repair. The variable-damping safe-landing mechanism was tested, showing the maximum falling speed can decrease from 1 to 0.1 m/s to promise safety, and the falling time can increase from about 5 to 45 s. Meanwhile, the rescue mechanism successfully retrieved the trapped robot. The results demonstrate the capabilities of the cable climbing robot and the feasibility of using the security and rescue mechanisms for the climbing robotic system, which have implications that the cable climbing robot with security and rescue mechanisms is more reliable and can be deployed in the real world with greater confidence.Publicationtype: Journal Article38 44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Autoencoder-based and physically motivated Koopman lifted States for wind farm MPC: a comparative case studyThis paper explores the use of Autoencoder (AE) models to identify Koopman-based linear representations for designing model predictive control (MPC) for wind farms. Wake interactions in wind farms are challenging to model, and have previously been addressed with Koopman lifted states. In this study we investigate the performance of two AE models: The first AE model estimates the wind speeds acting on the turbines these are affected by changes in turbine control inputs. The wind speeds estimated by this AE model are then used in a second step to calculate the power output via a simple turbine model based on physical equations. The second AE model directly estimates the wind farm output, i.e., both turbine and wake dynamics are modelled. The primary inquiry of this study is whether either of these two AE-based models can surpass previously identified Koopman models based on physically motivated lifted states. We find that the first AE model, which estimates the wind speed and hence includes the wake dynamics, but excludes the turbine dynamics outperforms the existing physically motivated Koopman model. However, the second AE model, which estimates the farm power directly, underperforms when the turbines' underlying physical assumptions are correct. We also investigate specific conditions under which the second, purely data-driven AE model can excel: Notably, when modelling assumptions, such as the wind turbine power coefficient, are erroneous and remain unchecked within the MPC controller. In such cases, the data-driven AE models, when updated with recent data reflecting changed system dynamics, can outperform physics-based models operating under outdated assumptions.Publicationtype: Conference Paper11