Robotic mechanisms can be driven by different internally and externally applied inertial and magnetic actuations. These actuations are utilized to regulate the dynamics of robots and move them in different locomotion modes. The first part of this dissertation is about using an external magnetic actuation to move a simple-in-design, small-scale robot for biomedical applications. The robots can be steered in different locomotion modes such as pivot walking and tumbling. The control design of this system consists of a swarm algorithm under a global control input, and a vision-based closed-loop controller to navigate in 2D environments.
Secondly, I propose a new Robust Nonlinear Quadratic Gaussian (RNQG) controller based on State-Dependent Riccati Equation (SDRE) scheme for continuous-time nonlinear systems. Existing controllers do not account for combined noise and disturbance acting on the system. The proposed controller is based on a Lyapunov function and a cost function includes; states, inputs, outputs, disturbance, and the noise acting on the system. We express the RNQG control law in the form of a traditional Riccati equation.
Real-time applications of a controller place a high computational burden on system implementation. This is mainly due to the nonlinear and complex form of the cost function. In order to solve this problem, the cost function is approximated by a weighted polynomial. The weights are found by using a least-squares technique and an offline neural network. The approximate cost function is incorporated into the controller by employing a method based on Bellman's principle of optimality. Finally, different examples are used to verify the utility of the proposed control approaches.
Finally, a highly disturbed and uncertain inertially actuated hopping robot is presented. Different nonlinear control schemes are used to regulate the system. The control outcomes are compared, and a foundation to design a robust optimal controller is given.
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Razzaghi, Pouria, "Actuation and Control Methods for Individual and Swarm Multiscale Mechanical Systems" (2021). Mechanical Engineering Research Theses and Dissertations. 37.