Linear model predictive control (MPC) assumes a linear system model, that the constraints sets are representable via linear inequalities and that the objective function is convex quadratic. Linear MPC is appealing because the associated optimisation problem - typically solved at each time interval - may be expressed as a convex quadratic program, which can be solved efficiently online. Topics emph{not} covered include methods for solving quadratic programs, soft constraints, terminal state constraints, closed-loop stability, closed-loop robustness and optimal reference trajectory calculation. A Matlab simulation example is included for illustration.