This paper presents a comparison of the performance of different control algorithms in two types of systems; one exhibiting fast dynamics and the other slow dynamics. The first control system regulates the speed of a DC motor, while the second control system regulates the temperature of an electrical heater. This systems' performance comparison pretends to evaluate the energy consumption, as well as the controllers' transient response in order to identify the best control strategy for each system. System models are obtained through the responses to a pseudorandom binary signal (PRBS) and the least squares fit method using an auto-regressive model with an exogenous variable (ARX). The implemented control algorithms used in this study are: Pole placement regulator (state-space controller) with integral error processing, auto-tunable proportional-integral-derivative (PID) controller, neural PID controller, unconstrained model predictive control (MPC), fuzzy PID controller, neuro-fuzzy controller, bayesian controller and an optimal quadratic regulator (LQR). A detailed analysis of the performance and energy consumption index is performed, that allow the categorization of the control strategies in accordance with their performance.