If you have a good linear model for your system you can pull out an LQR. It does not account for how bad your linearization of the true dynamics might be. Also

If you have a good linear model for your system you can pull out an LQR. It does not account for how bad your linearization of the true dynamics might be. Also from my experience you still need to tune the Q and R the same amount that you would P I and D. and I'm not convinced that it is strictly better at all. In industry at least PID absolutely dominates. It's also important to realize that just because the LQR is derived by solving an optimization problem, that doesn't mean it gives you the best possible gains for whatever you want. You got the optimal controller to minimize a quadratic cost you made up for a linearized approximation of your system. Iterative design (guess and check) is absolutely still the state of the art for control design, and using an LQR does not escape that.