The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs)is considered in the paper.Intuitively,a state-space transformation is performed such that sa...The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs)is considered in the paper.Intuitively,a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system.Then,considering that the leader’s information is not available to every follower,a novel distributed observer is designed to estimate the leader’s state using only exchange of information among neighboring followers.After that,a network of augmented systems is constructed by combining observers and followers dynamics.A nonquadratic cost function is then leveraged for each augmented system(agent)for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman(HJB)equation is solved in a data-based fashion.More specifically,a data-based off-policy reinforcement learning(RL)algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents’dynamics.Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated.Finally,the correctness and validity of the theoretical results are demonstrated by a simulation example.展开更多
To reduce the risk of infection in medical personnel working in infectious-disease areas, we proposed ahyper-redundant mobile medical manipulator (HRMMM) to perform contact tasks in place of healthcare workers.A kinem...To reduce the risk of infection in medical personnel working in infectious-disease areas, we proposed ahyper-redundant mobile medical manipulator (HRMMM) to perform contact tasks in place of healthcare workers.A kinematics-based tracking algorithm was designed to obtain highly accurate pose tracking. A kinematic modelof the HRMMM was established and its global Jacobian matrix was deduced. An expression of the trackingerror based on the Rodrigues rotation formula was designed, and the relationship between tracking errors andgripper velocities was derived to ensure accurate object tracking. Considering the input constraints of the physicalsystem, a joint-constraint model of the HRMMM was established, and the variable-substitution method was usedto transform asymmetric constraints to symmetric constraints. All constraints were normalized by dividing bytheir maximum values. A hybrid controller based on pseudo-inverse (PI) and quadratic programming (QP) wasdesigned to satisfy the real-time motion-control requirements in medical events. The PI method was used whenthere was no input saturation, and the QP method was used when saturation occurred. A quadratic performanceindex was designed to ensure smooth switching between PI and QP. The simulation results showed that theHRMMM could approach the target pose with a smooth motion trajectory, while meeting different types of inputconstraints.展开更多
基金supported in part by the National Natural Science Foundation of China(61873300,61722312)the Fundamental Research Funds for the Central Universities(FRF-MP-20-11)Interdisciplinary Research Project for Young Teachers of University of Science and Technology Beijing(Fundamental Research Funds for the Central Universities)(FRFIDRY-20-030)。
文摘The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs)is considered in the paper.Intuitively,a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system.Then,considering that the leader’s information is not available to every follower,a novel distributed observer is designed to estimate the leader’s state using only exchange of information among neighboring followers.After that,a network of augmented systems is constructed by combining observers and followers dynamics.A nonquadratic cost function is then leveraged for each augmented system(agent)for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman(HJB)equation is solved in a data-based fashion.More specifically,a data-based off-policy reinforcement learning(RL)algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents’dynamics.Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated.Finally,the correctness and validity of the theoretical results are demonstrated by a simulation example.
基金the National Natural Science Foundation of China(No.52175103)。
文摘To reduce the risk of infection in medical personnel working in infectious-disease areas, we proposed ahyper-redundant mobile medical manipulator (HRMMM) to perform contact tasks in place of healthcare workers.A kinematics-based tracking algorithm was designed to obtain highly accurate pose tracking. A kinematic modelof the HRMMM was established and its global Jacobian matrix was deduced. An expression of the trackingerror based on the Rodrigues rotation formula was designed, and the relationship between tracking errors andgripper velocities was derived to ensure accurate object tracking. Considering the input constraints of the physicalsystem, a joint-constraint model of the HRMMM was established, and the variable-substitution method was usedto transform asymmetric constraints to symmetric constraints. All constraints were normalized by dividing bytheir maximum values. A hybrid controller based on pseudo-inverse (PI) and quadratic programming (QP) wasdesigned to satisfy the real-time motion-control requirements in medical events. The PI method was used whenthere was no input saturation, and the QP method was used when saturation occurred. A quadratic performanceindex was designed to ensure smooth switching between PI and QP. The simulation results showed that theHRMMM could approach the target pose with a smooth motion trajectory, while meeting different types of inputconstraints.
