187 research outputs found

    Robust hierarchical adaptive fuzzy relative motion coordination for feature points of two rigid bodies with input and output constraints

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    A model-based six-degrees-of-freedom relative motion coordinated control approach is developed for the relative position tracking and attitude synchronization between feature points of two rigid bodies subject to control input constraints, output constraints and model uncertainties. In the designing framework of adaptive backstepping control technique, the control input saturation is compensated by the nonlinear antiwindup compensator and the output constraints are handled by the barrier Lyapunov function-based backstepping design. The unknown misalignment vector of the feature point with respect to the center of the mass for the chaser is estimated by the element-wise adaptive law, while the model uncertainties and unknown dynamical couplings are compensated by the adaptive hierarchical fuzzy logic system to decrease the computational burden with respect to the traditional adaptive fuzzy system. The ultimately uniformly bounded convergence of the relative pose and relative velocities is analyzed in the Lyapunov framework and the effectiveness of the proposed approach is validated by the numerical simulation

    Saturated adaptive relative motion coordination of docking ports in space close-range rendezvous

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    An adaptive relative pose controller for docking ports of two uncertain spacecraft in autonomous rendezvous and docking is developed. A novel relative translational and rotational model represented in the chaser body-fixed frame is derived firstly based on the classical Newton-Euler equations. Based on the proposed model, a six-degrees-of-freedom adaptive control law is presented based on norm-wise estimations for the unknown parameters of two spacecraft to decrease the online computational burden. Meanwhile, an adaptive robust control input is designed by introducing an exponential function of states to improve the response performance with respect to the traditional adaptive robust control. Moreover, a linear anti-windup compensator is employed to ensure the bounded performance of the control inputs. The explicit tuning rules for designing parameters are derived based on the stability analysis of the closed-loop system. It is proved in Lyapunov framework that all closed-loop signals are always bounded and the pose tracking error ultimately converges to a small neighborhood of zero. Simulation results validate the performance of the proposed robust adaptive control strategy

    Adaptive control of space proximity missions with constrained relative states, faults and saturation

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    This paper studies a relative position and relative orientation control problem of close-range spacecraft proximity missions under control input saturation, actuator faults, relative state constraints, kinematic couplings, parametric uncertainties, and unknown external disturbances. The problem of control input saturation is handled with introducing the outputs of an augmented system into the controller, and relative state constraints are guaranteed by using barrier Lyapunov function in backstepping design. Actuator faults in dynamical model are compensated by element-wise adaptive estimations, while unknown dynamic couplings, parametric uncertainties, and unknown bounded disturbances are compensated by norm-wise adaptive estimations. Based on the developed adaptive nonlinear control strategy, relative motion states uniformly ultimately tend to small adjustable neighborhoods of zero, and if the initial relative states are constrained in the predefined ranges, then relative state constraints will never be violated. Simulation comparison validates the advantages of the control strategy

    Disturbance observer-based saturated fixed-time pose tracking for feature points of two rigid bodies

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    The relative pose dynamics for the feature points of two nearby rigid bodies is modeled in the body-fixed coordinated system of the controlled chaser. Then, a novel model-based fixed-time relative motion controller is developed for the active chaser to achieve the target’s feature-point position tracking and attitude synchronization under constrained control inputs and model uncertainties. In particular, the convergence time of system states is prescribed independently with initial states and estimated by the control parameters. A fixed-time disturbance observer is introduced to estimate and compensate unknown bounded disturbances, and the observation errors converge to zero in fixed time. A novel fixed-time anti-windup compensator is proposed to deal with the actuator saturation. Theoretical analysis and simulation results validate the effectiveness of the proposed controller.</p

    Adaptive guidance and control of uncertain lunar landers in terminal landing phases

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    A novel double-loop guidance and control strategy for under-actuated lunar landers in terminal landing phases is developed by using adaptive nonlinear control approach in this study. To derive the main thrust input and inner-loop desired attitude trajectory, the outer-loop position tracking guidance law is firstly developed based on the hyperbolic tangent functions to guarantee the constrained thrust and singularity avoidance of the desired attitudes. Then, to avoid the complicated analytic-derivative computing of the desired attitude trajectory, a stable second-order filter is employed to generate the command attitude trajectory for the inner-loop attitude motion. Finally, an adaptive attitude tracking controller is designed by combining the barrier Lyapunove function and the backstepping technique to get rid of the singularities of the Euler angles-based attitude kinematic Jacobian matrix. In addition, tuning rules for designing parameters in guidance law and attitude controller are derived based on the Lyapunov analysis, and the pose tracking errors in the closed-loop system ultimately converge to the small neighborhoods of the origin. An example is simulated to verify the effectiveness of the proposed control design approach

    Dual quaternion-based moving target trajectory tracking adaptive sliding mode control for robotic manipulator

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    This article focuses on the moving target trajectory tracking control problem for robotic manipulator. The dynamics models of the rigid-body moving target and the manipulator end-effector based on the unit dual quaternion are firstly established. Then, the relative motion dynamics equation is deduced according to the arithmetic rules of dual quaternion. Further, an adaptive sliding mode controller is put forward to guarantee that the error between the pose of the rigidbody moving target and the pose of manipulator end-effector asymptotically converges to zero, where the upper bound on the norm of the uncertainty in the second-order differential error dynamics equation is estimated online by adaptive law. It is ensured via the Lyapunov theory that the asymptotic stability of the closed-loop system. Numerical simulation validates the theoretical consequences.</p

    Finite-time relative pose tracking control for uncertain spacecraft rendezvous and docking

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    This study investigates the robust finite-time pose tracking control for spacecraft autonomous rendezvous and docking with parametric uncertainties and bounded external disturbances. Based on the uncertainly coupled relative pose dynamics, a fast terminal sliding mode controller is developed to achieve the finite-time convergence of the pose tracking errors. To reduce the control chattering results from the signum function in the controller, an exponential reaching law is employed to achieve the decreasing of the reaching time towards the sliding surface. The explicit tuning rules for designing parameters are derived based on the stability analysis of the closed-loop system. It is proved in Lyapunov framework that all closed-loop signals are always kept bounded and the pose tracking error converges to small neighborhood of zero in finite time. Simulation results validate the performance of the proposed robust finite-time control strategy

    Mean ROC values over 76 tasks for each kernel on the von Mering Data Set

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    The horizontal axis denotes the -values used to build the corresponding kernel and the vertical axis is the mean ROC values.<p><b>Copyright information:</b></p><p>Taken from "Adaptive diffusion kernel learning from biological networks for protein function prediction"</p><p>http://www.biomedcentral.com/1471-2105/9/162</p><p>BMC Bioinformatics 2008;9():162-162.</p><p>Published online 25 Mar 2008</p><p>PMCID:PMC2409449.</p><p></p

    Best ROC values for diferent tasks achieved by different kernels on the von Mering Data Set

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    The horizontal axis represents the tasks and the vertical axis is the corresponding best ROC values.<p><b>Copyright information:</b></p><p>Taken from "Adaptive diffusion kernel learning from biological networks for protein function prediction"</p><p>http://www.biomedcentral.com/1471-2105/9/162</p><p>BMC Bioinformatics 2008;9():162-162.</p><p>Published online 25 Mar 2008</p><p>PMCID:PMC2409449.</p><p></p

    PPUM-aided receding horizon optimization for robot path planning in uncertain environment

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    The ability to understand spatialtemporal patterns for crowds of people is crucial for achieving long-term autonomy of mobile robots deployed in human environments. The traditional historical data-driven memory models may be inadequate due to their limitations in adapting to anomalies or unexpected events in crowd behavior. In this article, a Receding Horizon Optimization (RHO) formulation is proposed that incorporates a Probability-related Partially Updated Memory (PPUM) for robot path planning in crowded environments with uncertainties. The PPUM acts as a memory layer that combines real-time sensor observations with historical knowledge using a weighted evidence fusion theory to improve robot’s adaptivity to the dynamic environments. RHO then utilizes the PPUM as informed knowledge to generate a path that minimizes the likelihood of encountering dense crowds while reducing the cost of local motion planning. The proposed approach provides an innovative solution to the problem of robot’s long-term safe interaction with humans in crowded environments with anomalies. In simulations, the results demonstrate the superior performance of our approach compared to benchmark methods in terms of crowd distribution estimation accuracy, adaptability to anomalies, and path planning efficiency.</p
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