Haiyun Zhang

This article presents a model predictive control (MPC) method for humanoid dual-arm robots (DARs) to realize hybrid force-velocity control in the task space. The proposed method, named hybrid force-velocity MPC (HFV-MPC), enables simultaneous tracking of velocity and external force in the task space, as well as internal force regulation. First, a synchronous decoupling model of internal force, external force, and velocity is developed to address the limitations of conventional methods that decouple internal and external forces solely in the end-effector space and fail to meet the independent regulation requirements of external force and velocity in the task space. Furthermore, a generalized velocity model that integrates task-space velocity, external force, and internal force was established, and a HFV-MPC framework is constructed to enhance system robustness and achieve precise dual-arm force–velocity tracking. Subsequently, a comprehensive MPC cost function is designed, incorporating a dual-arm motion coordination coefficient to suppress undesired redundant joint motions and improve whole-body motion stability. In addition, a feedforward-linearized incremental system is derived, where the incremental model and force prediction jointly construct a generalized state-space model for MPC, to balance modeling accuracy and computational efficiency. Finally, the proposed method is validated in terms of effectiveness and robustness through both simulation and physical robotic experiments.

As humans focus control only on task–space variables to achieve dexterous manipulation, robots could strongly profit from advanced task-space control, which is still blocked by kinematic and dynamic uncertainties now. This article proposes an adaptive composite observer (ACO) with finite-time sliding-mode manifold to compensate uncertain kinematics and dynamics synthetically, then develops a novel nonsingular terminal sliding mode controller based on an adaptive neural network (NN) to stabilize the task–space tracking errors directly with prescribed performance. The global finite-time stability of the entire observer–controller system is achieved by Lyapunov method, and the observer and controller errors will converge to zeros in finite time with preassigned transient and steady-state performance. Simulation and experimental studies are also presented to verify the effectiveness of the designed control methods.

This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa on a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.

We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics, mismatches, and di

The potential field (PF) method can be used for path planning in the 3-D space.

Robotic manipulators have captured the attention of many specialists owing to its importance in academic research and industrial automation. This paper proposes a novel hierarchical self-organizing fuzzy optimal controller for the trajectory tracking control of robotic manipulator systems. The hierarchical self-organizing fuzzy optimal controller employs a self-organizing fuzzy logic system as a superior control strategy regulator for a subordinate optimal tracking controller. Using the self-organizing learning and fuzzy inference operation, the weighting matrix in the optimal controller is configured adaptively according to the robotic dynamical behavior. The optimal tracking control law under this hierarchical architecture is derived using the maximum principle. Stability and robustness of the hierarchical self-organizing fuzzy optimal controller are then analyzed and proved through the Lyapunov stability approach and Barbalat's Lemma. A simulation study demonstrates the effectiveness and feasibility of this hierarchical system, and compares it with a self-organizing fuzzy controller.

In this study we characterized and investigated the specific phenomenon of “companion drops” in the drop-on-demand (DOD) ink jetting process. A series of simulations based on a piezoelectric DOD printhead system is presented, adapting the volume-of-fluid (VOF) interface-capturing method to track the boundary evolution and model the interfacial physics. The results illustrate the causality between the generation of companion drops and droplet deviation behavior, as well as their close correlations with ink jetting straightness and printing accuracy. The characteristics of companion drops are summarized and compared with those of satellite drops. Also, a theoretical mechanism for the generation of companion drops is presented, and their effects and behaviors are analyzed and discussed. Finally, the effects of critical factors on the generation of companion drops are investigated and characterized based on variations in the printable pressure range. Recommendations are given for the suppression of companion drops and for the improvement of printing accuracy.