# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 4 Conclusions  
The methods employed in this work showed that a visual force sensor could provide good force feedback when applied to the physical operation of a soft robotic gripper. The force sensor achieved a mean squared error of approximately one Newton squared for a novel object. These results are comparable to those found in [18], where a more computationally intensive network was used. It is difficult to directly compare results, as the max force value for this work was approximately thirty Newtons, compared to four Newtons in [18] and much of the other work. The major limitation of this force sensor, as presented in this report, is its inability to be completely robust to occlusions of the fin.  
Figure 16: Gripping test using learned parameters found in Figure 14.  
The simulated occlusions during training helped significantly in this regard but could only do so much. When severe occlusions occurred in the gripping process, the gripping force appeared to saturate early, potentially resulting in unreliable reading. Due to the need for perspective warping to standardize the captured images' viewing angle, it was crucial to calibrate the camera position and warping source points to create the desired final image. This moderately tedious process had to be re-calibrated with every camera bump. A potential solution to this limitation is mentioned in the conclusion section of the future work.  
An adaptive force controller was designed to implement visual force feedback into a closed-loop control scheme to further investigate the effectiveness of a visual force sensor. The results show the algorithm learning controller gains and the system nonlinearity over time, resulting in a control of the system that closely matches a designed reference model. Qualitatively examining the controller results, the system can follow the reference model, giving the results desired by a closed-loop controller. One drawback and limitation of this method was the inability of the model to accurately match the derivative state of the reference model due to noisy measurements, resulting in the need to limit the error signal at lower values. Limiting the controller in this way prevents parameter drift but creates a permanent model-tracking error in the system. A further limitation of the control scheme was the time required to achieve acceptable control performance. The first few grip cycles were prolonged, resulting in poor control performance. The learning rates were set to small values, as increases often led to instability in the system and the gains not converging. A further improvement to the gripping architecture would be the implementation of a more complex control algorithm for the position control of the gripper. The current structure uses a PI controller for position control, which, in this system, is the controlled variable of the force controller.  
In conclusion, a model reference adaptive controller was designed and implemented using visual force feedback to control the applied force by a soft robotics gripper. The solution used an autoencoder and a regression algorithm to estimate the force optically, and an MRAC controller effectively regulated the desired force after a few grip and release cycles of the gripper. Using model reference adaptive control and visual force sensing has several vital implications in soft robotics. First, a visual force sensor can remove the need for embedding sensors into the gripper's soft material, reducing the manufacturing time and implementation complexity. Cameras are simple to set up and allow for an intuitive approach to sensing. An effective closed-loop control strategy allows for precise and delicate gripping of potentially fragile parts and can create a more efficient gripping process.