I have been accepted by a professor for an undergraduate research opportunity in the area of surgical robotics, where I will be working with a Ph.D. student in control and applying some machine learning to make control easier.

A bit about my self - I have done college courses on classical and modern control theory and have a good understanding of nonlinear, adaptive and optimal control along with classical techniques like Bode, Nyquist plot. I have also worked on a couple of projects like quadrotor control using lqr, full state feedback, $H_{\infty}$ and SMC. I have also worked on controlling a robotic arm. I haven't really worked on hardware though; everything has been simulations in Matlab or Python.

What else should I do or concepts that I should learn before I start the research intern, so I won't lag behind on any concepts or skills and won't be a bother to the Ph.D./professor?

I have worked on a couple of projects like control of a quadrotor/ self balancing robot/ 6 dof robotic arm using nonlinear, optimal and adaptive control. So, in all these projects I have first basically tried to understand the dynamics of the system, then I modeled it. Next I applied different control strategies and compared their responses and chose the best one.

My question is when approaching problems in industry or in research, is the approach for these simple bots fine or are there other things that I should consider.

EDIT: Can someone who has worked/ has experience in this area, put a step by step approach that should be done to approach any control problem in any field. With the necessary concepts and skills needed.

I have been accepted by a professor for an undergraduate research opportunity in the area of surgical robotics, where I will be working with a Ph.D. student in control and applying some machine learning to make control easier.

A bit about my self - I have done college courses on classical and modern control theory and have a good understanding of nonlinear, adaptive and optimal control along with classical techniques like bode, Nyquist plot. I have also worked on a couple of projects like Quadrotor control using lqr, full state feedback, H infinity and SMC. I have also worked on controlling a robotic arm. I haven't really worked on hardware though, everything has been simulations on Matlab or python.

What else should I do or concepts that I should learn before I start the research intern, so I won't lag behind on any concepts or skills and won't be a bother to the phd/professor?

I have been accepted by a professor for an undergraduate research opportunity in the area of surgical robotics, where I will be working with a Ph.D. student in control and applying some machine learning to make control easier.

A bit about my self - I have done college courses on classical and modern control theory and have a good understanding of nonlinear, adaptive and optimal control along with classical techniques like Bode, Nyquist plot. I have also worked on a couple of projects like quadrotor control using lqr, full state feedback, $H_{\infty}$ and SMC. I have also worked on controlling a robotic arm. I haven't really worked on hardware though; everything has been simulations in Matlab or Python.

What else should I do or concepts that I should learn before I start the research intern, so I won't lag behind on any concepts or skills and won't be a bother to the Ph.D./professor?

I have been accepted by a professor for an undergraduate research opportunity in the area of surgical robotics, where I will be working with a PHD student in control and applying some machine learning to make control easier.

A bit about my self - I have done college courses on classical and modern control theory and have a good understanding of nonlinear, adaptive and optimal control along with classical techniques like bode, nyquist plot. I have also worked on a couple of projects like Quadrotor control using lqr,full state feedback, H infinity and SMC. I have also worked on controlling a robotic arm. I haven't really worked on hardware though, everything has been simulations on matlab or python.

What else should I do or concepts that I should learn before I start the research intern, so I won't lag behind on on any concepts or skills and won't be a bother to the phd/professor?

I have been accepted by a professor for an undergraduate research opportunity in the area of surgical robotics, where I will be working with a Ph.D. student in control and applying some machine learning to make control easier.

A bit about my self - I have done college courses on classical and modern control theory and have a good understanding of nonlinear, adaptive and optimal control along with classical techniques like bode, Nyquist plot. I have also worked on a couple of projects like Quadrotor control using lqr, full state feedback, H infinity and SMC. I have also worked on controlling a robotic arm. I haven't really worked on hardware though, everything has been simulations on Matlab or python.

What else should I do or concepts that I should learn before I start the research intern, so I won't lag behind on any concepts or skills and won't be a bother to the phd/professor?