Haibin Yin

Improved tracking control for an SMA actuator using controller based on error and inverse segmented model

In this paper, a controller based on error and the inverse segmented model, is used to control the position of an SMA actuator. First an inverse model using three segments for both heating and cooling processes is built and experiments are used to validate the model and to identify the parameters. Then a controller, which is based on inverse segmented model and the value of error between the reference and actual displacement, is proposed in the position control process. Furthermore, experimental results demonstrate that the proposed controller performs better than both traditional PID controller and inverse segmented model controller.

Position control of a robot finger with variable stiffness actuated by shape memory alloy

The purpose of this research is to present a new method to achieve precise position tracking control for robot finger with variable stiffness mechanism. First, the variable stiffness design of the finger which is actuated by SMA-3 fibers is presented. Then, the relationship between the variation of pulling force ΔF of the bending robot finger and input voltage of SMA-3 is established. In addition, the relationship between the pulling force F and the input voltage of SMA-2 wire is established as well. Therefore, a control method based on these models for the position tracking of robot finger with variable stiffness characteristics is proposed. The experimental results show that the proposed method has better performance than traditional PID control when the stiffness changed by heating current, resulting in a reduction of maximum error by 86%.

An overall structure optimization for a light-weight robotic arm

The application of lightweight robotic arms is having been expanded from factory automation to the service areas. It is significant to make lightweight robotic arms much safer and lighter for human-robot co-existence. This study presents an overall structure optimization design approach to minimize the mass of the lightweight robotic arm with the constraints on structural strength and drive trains. This method is implemented by integrated co-simulation analysis and the complex method. In this paper, a parameterized model of the robotic arm is established for optimization design. Then, the complex method is utilized to obtain the optimal design of the robotic arm. Meanwhile, a co-simulation analysis platform based on ANSYS Workbench and ADAMS is built to evaluate the constraints. Finally, a design example is illustrated to demonstrate the application of the proposed approach in design of a five degree-of-freedom lightweight robotic arm.

Investigate of Grasping Force for a Soft Robot Hand Under Pulling Force and Varying Stiffness

The purpose of this research is to analyze and test grasping force of soft robot hand with variable stiffness, as the essential part of the robot hand, each finger is composed of 7 SMA fibers, five of which are used as skeleton of finger with variable stiffness while others provide pulling force. Firstly, the structure and materials of the fingers are introduced. Secondly, the computations of grasping forces and stiffness of fingers were implemented based on Cosserat theory and stiffness model, respectively. Moreover, the variable stiffness of fingers was measured by applying the different heating currents for the SMA simultaneously. Results indicates that the increase of stiffness around 61.7% from the low stiffness to high stiffness. Finally, the pulling forces with the variable stiffness and different grasping forces were measured, which demonstrate that grasping forces can be adjusted by varying stiffness.

Double-Parameter Regression Design of Drive Trains for Lightweight Robotic Arms

This paper presents a new design approach for lightweight robotic arms. In this method, the drive trains and structural dimensions are parameterized as design variables, and a major objective is to minimize the total mass of robotic arms satisfying the constraint conditions. To solve the optimization problem, the relationship among mass, the moment of inertia and torque of drive trains are introduced as their power-density curves, which is the basis of the double-parameter regression design. In this design approach, there are two modules: structure optimization and drive trains optimization. The orthogonal design method is adopted to implement the structure optimization. The double-parameter regression design is used for drive trains optimization. Finally, a design example for a four degree of freedom (DOF) robotic arm is demonstrated to verify the validity of the proposed scheme.

A unified design for lightweight robotic arms based on unified description of structure and drive trains

This article presents a unified design for lightweight robotic arms based on a unified description of structure and drive trains. In the unified design, the drive trains and structural dimensions are parameterized as design variables, and a major objective minimizes the total mass of robotic arms satisfying the constraint conditions and design criteria. To implement the optimization problem, a mapping relationship between mass and torque of drive trains is introduced as their power–density curves, which enable a unified description of structure and drive trains combining with the dynamics of robotic arms. In this implementation of unified design, there are two modules: structure optimization and drive trains design. The finite element method with nonlinear programming by quadratic Lagrange algorithm is adopted to implement the structure optimization. Moreover, the dynamic analysis in MSC ADAMS is achieved to design the drive trains of robotic arms. This method could uniformly evaluate all components of robotic arms in mass and continuously search the global optimal results. Finally, a design example on this unified design is compared with a referenced design to illustrate the validity and advantage of the proposed scheme.