This chapter covers control theory. Optimal control balances the performance and robustness of the closed-loop system with the cost of control. This results in an optimization problem constrained by the dynamics. This chapter begins with linear optimal control, including the linear quadratic regulator (LQR) and Kalman filter. These both involve the Riccati equation, which we will derive using Lagrange multipliers. We then explore nonlinear optimal control, deriving the Hamilton-Jacobi-Bellman (HJB) equations. This involves reinforcement learning and dynamic programming, which are optimization frameworks to solve this nonlinear control problem. Next, we develop model predictive control (MPC) as an extension to optimal control for a broader class of control tasks. MPC is widely used in industrial control due to its flexibility, robustness, ease of implementation, and ability to respect constraints. MPC repeatedly solves an optimal control problem on a receding horizon, reinitializing the optimization problem as new sensor information is available. Finally, we discuss linear matrix inequalities, which allow us to reformulate many problems in control as convex optimization problems.