This article proposes a novel interaction force estimation scheme capable of estimating the interaction forces for industrial robots with high precision in the absence of force sensors. Specifically, we first establish the dynamic model of a class of industrial robots via model identification methods. Furthermore, a high-order finite-time observer (HOFTO) is established to estimate the interaction forces, where HOFTO serves as an interaction force observer and is designed to accurately estimate external time-varying interaction forces. In addition, we strictly analyze the stability of our approach in two different cases, endowing HOFTO with reliable estimation abilities in broader applications. Subsequently, we integrate the estimated force information into the applications of force control frameworks (e.g., collision detection and impedance control). Several evaluations on a real 6-axes robot demonstrate the effectiveness of the proposed approach.

Nowadays, the realization of obstacle avoidance for robot manipulators are generally based on offline path planning, which may be insufficient for real-time dynamic obstacle avoidance scenarios. In order to achieve safe and smooth avoidance of dynamic obstacles, a low-latency motion planning algorithm, which takes into account the dynamic planning is of practical significance. Toward this end, this article proposes a cascaded nonlinear model predictive control (MPC) assigned with a two-stage optimization problem. Specially, the high-level MPC combines artificial potential field as a motion planner to generate foresight smooth trajectories. The low-level MPC acts as a trajectory tracker and a safety protector, taking along hard constraints to avoid collisions and singularities. In addition, a super-twisting observer is deployed to enhance the motion estimation accuracy of dynamic obstacles. Experimental results show that the proposed approach is beneficial to achieve safe and smooth dynamic obstacle avoidance in real-world scenarios.

Robust locomotion is a challenging task for humanoid robots, especially when considering dynamic disturbances. This article proposes a disturbance observer-based cascaded model predictive control (MPC) approach for bipedal locomotion, with the capability of exploiting ankle, stepping, hip and height variation strategies. Specifically, based on the variable-height inverted pendulum model, a nonlinear MPC that is run at a low frequency is built for 3-D locomotion (i.e., with height variation) while accounting for the footstep modulation as well. Differing from previous works, the nonlinear MPC is formulated as a convex optimization problem by semidefinite relaxation. Subsequently, assuming a flywheel at the pelvis center, a linear MPC that is run at a high frequency is proposed to regulate angular momentum (e.g., through rotating the upper body), which is solved by convex quadratic programming. To run the cascaded MPC in a closed-loop manner, a high order sliding mode observer is designed to estimate system states and dynamic disturbances simultaneously. Simulation and hardware experiments demonstrate the walking robustness in real-world scenarios, including 3-D walking with varying speeds, walking across non-coplanar terrains and push recovery.

This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.

The mechanical design of a robot often influences the choice of control strategy, especially for high-dimensional manipulator systems with multiple inputs and outputs. Striking a balance between hardware and software, it remains a significant challenge to design a control framework that is both easy to implement and high performing. This article addresses this concern by developing a systematic control architecture for trajectory tracking problems, focusing solely on position measurements. The approach involves constructing a dynamic model of the manipulator in the joint space through parameter identification techniques. A nonsmooth observer is then devised to estimate unmeasured states, unknown disturbances, and uncertain nonlinear functions in real time, which are incorporated into a nonsmooth feedback control design to provide a control solution. The stability of the system is ensured using the homogeneous system theory and Lyapunov theorems. To validate the effectiveness and feasibility of the proposed tracking control approach, extensive evaluations are conducted on a six-degree-of-freedom manipulator, including tests for tracking performance and repeatability.

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.