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The following research proposes a closed loop force control system, which is implemented on a soft robotic prosthetic hand. The proposed system uses a force sensing approach that does not require any sensing elements to be embedded in the prosthetic's fingers, therefore maintaining their monolithic structural integrity, and subsequently decreasing the cost and manufacturing complexity. This is achieved by embedding an aluminum test specimen with a full bridge strain gauge circuit directly inside the actuator's housing rather than in the finger. The location of the test specimen is precisely at the location of the critical section of the bending moment on the actuator housing due to the tension in the driving tendon. Therefore, the resulting loadcell can acquire a signal proportional to the prosthetic's grasping force. A PI controller is implemented and tested using this force sensing approach. The experiment design includes a flexible test object, which serves to visually demonstrate the force controller's performance through the deformation that the test object experiences. Setpoints corresponding to "light", "medium", and "hard" grasps were tested with pinch, tripod, and full grasps and the results of these tests are documented in this manuscript. The developed controller was found to have an accuracy of ±2%. Additionally, the deformation of the test object increased proportionally with the given grasp force setpoint, with almost no deformation during the light grasp test, slight deformation during the medium grasp test, and relatively large deformation of the test object during the hard grasp test.

The soft pneumatic actuators of an assistive exoskeleton hand glove are here designed. The design of the actuators focuses on allowing the actuator to perform the required bending and to restrict elongation or twisting of the actuator. The actuator is then modeled using ABAQUS/CAE, a finite element modeling software, and the open loop response of the model is obtained. The parameters of the actuator are then optimized to reach the optimal parameters corresponding to the best performance. Design of experiment (DOE) techniques are then approached to study the robustness of the system. Software-in-the-loop (SiL) is then approached to control the model variables via a proportional-integral-derivative (PID) control generated by FORTRAN code. The link between the two programs is to be achieved by the user subroutine that is written, where the subroutine receives values from ABAQUS/CAE, performs calculations, and passes values back to the software. The controller's parameters are tuned and then the closed loop response of the model is obtained by setting the desired bending angle and running the model. Furthermore, a concentrated force at the tip of the actuator is added to observe the actuator's response to external disturbance.