Controls Engineering

Controls engineering designs systems that drive measured quantities toward a desired behavior — speed, position, temperature, pressure — using feedback and feedforward.

Overview

Classical control treats single-input/single-output (SISO) linear systems with transfer functions. Modern control adds state-space, multivariable, optimal, robust, and adaptive methods.

System Types

• Linear vs nonlinear.
• Continuous- vs discrete-time.
• Time-invariant vs time-varying.
• SISO vs MIMO.
• Open- vs closed-loop.

Analysis & Tools

• Transfer function G(s), block diagrams, signal-flow.
• Poles, zeros, and stability (LHP poles).
• Step / impulse / frequency response.
• Bode plot — gain & phase margin.
• Nyquist criterion for stability.
• Root locus — closed-loop pole movement with gain.

Controller Design

• P, PI, PID, PI with derivative on PV, two-degree-of-freedom.
• Lead, lag, lead-lag compensators.
• State-feedback & observer (pole placement, LQR/LQG).
• Model Predictive Control (MPC) for constrained MIMO.
• Adaptive / gain-scheduled control for nonlinear plants.
• Feedforward to cancel measurable disturbances.

Discrete-Time / Digital

• Sample rate ≥ 10× closed-loop bandwidth (rule of thumb).
• Z-transform; pole placement in the unit disk.
• Anti-aliasing filter before ADC.
• Tustin / bilinear transform for s → z.
• Fixed-point vs floating-point trade-offs on microcontrollers.

Software

• MATLAB / Simulink (Control System Toolbox, Simulink Control Design).
• Python — python-control, scipy.signal, slycot.
• Maple, Mathematica, Octave.
• System identification: MATLAB SI Toolbox, SIPPY.
• HIL / RCP: dSPACE, Speedgoat, NI VeriStand, Simulink Real-Time.