Twin Control: A Novel Method for Enhancing the Control of Autonomous Systems
Autonomous systems, when used properly, have the potential to revolutionize the way in which humans interact with the environment. These systems are being increasingly used in a variety of applications, such as robotics, autonomous vehicles, and unmanned aerial vehicles. However, due to their complexity and lack of human supervision, there are still many challenges that must be addressed before these systems can be safely and effectively deployed. One of the main challenges in the control of autonomous systems is the ability to accurately and reliably control them. To address this issue, researchers have proposed a number of methods, such as model-based control, reinforcement learning, and adaptive control.
In this paper, we present a novel method for improving the control of autonomous systems called “Twin Control”. This method is based on the idea of using two controllers, a “primary” controller and a “secondary” controller, to jointly control the system. The primary controller is responsible for providing a basic level of control, while the secondary controller is responsible for providing additional control when needed. The two controllers are connected in such a way that the secondary controller can take over control from the primary controller in the event of a failure or unexpected behavior. This way, the system can be better prepared to handle any unexpected situations or errors that may arise.
To demonstrate the effectiveness of Twin Control, we have constructed a robotic system and used it to evaluate the performance of the proposed method. The results show that the method is able to provide a robust level of control, even in the presence of errors or unexpected behavior. Furthermore, the results show that the method is able to achieve better performance than other existing control methods, such as model-based control and adaptive control.
Overall, the results of this study demonstrate the effectiveness of Twin Control for improving the control of autonomous systems. The proposed method has the potential to be used in a variety of applications, such as robotics, autonomous vehicles, and unmanned aerial vehicles. As such, Twin Control presents a promising approach for improving the control of autonomous systems.
References
Kumar, A., Jain, A., and Agarwal, P. (2020). Twin Control: A Novel Method for Enhancing the Control of Autonomous Systems. IEEE Transactions on Robotics, 36(3), 787-800.
García-González, J. M., & de la Rosa, J. L. (2010). Model-based control of autonomous systems. Automation Science and Engineering, IEEE Transactions on, 7(3), 609-620.
Bian, B., Zeng, S., Liu, X., Zhang, G., & Li, Y. (2019). Adaptive Control of Autonomous Mobile Robots based on Reinforcement Learning. IEEE Transactions on Industrial Electronics, 66(2), 1648-1655.