Process Modelling for Control: A Unified Framework Using Standard Black-box Techniques

| ARMAX | Auto-Regressive Moving-Average with eXogenous in-puts | |
| ARX | Auto-Regressive with eXogenous inputs | |
| BJ | Box-Jenkins | |
| EDF | lectricit de France | |
| FIR | Finite Impulse Response | |
| GPC | Generalised Predictive Control | |
| LFT | Linear Fractional Transformation | |
| LMI | Linear Matrix Inequality | |
| LQG | Linear Quadratic Gaussian | |
| LTI | Linear Time Invariant | |
| MIMO | Multi-Input Multi-Output | |
| MISO | Multi-Input Single-Output | |
| MPC | Model-based Predictive Control | |
| OE | Output Error | |
| PI | Proportional Integral | |
| PID | Proportional Integral Derivative | |
| PRBS | Pseudo-Random Binary Signal | |
| PWR | Pressurised Water Reactor | |
| QFT | Quantitative Feedback Theory | |
| SISO | Single-Input Single-Output | |
| SNR | Signal-to-Noise Ratio | |
| w.p. | with probability | |
| w.r.t. | with respect to |
| d( t) | measured and/or unmeasured disturbances vector | |
| e( t) | white noise signal | |
| f( t) | controller output signal in the general closed-loop representation | |
| g( t) | controller input signal in the general closed-loop representation | |
| h( t) | controller input signal in an LFT | |
| l( t) | controller output signal in an LFT | |
| p( t) | step disturbance signal | |
| r( t) | reference signal | |
| r 1( t) | reference signal | |
| r 2( t) | reference or feed-forward signal | |
| u( t) | input signal | |
| ?( t) | stochastic disturbance signal | |
| w( t) | exogenous input signal of an LFT | |
| x( t) | state vector of a system | |
| y( |