Model-free control, along with intelligent PID controllers (iPIDs), is introduced in this paper for the first time in an unified manner, incorporating recent advances. The basics of model-free control utilize old functional analysis and elementary differential algebra. Estimation techniques are straightforward via recent online parameter identification methods. The importance of iPIDs, especially iPs, is derived from the presence of friction. The widespread use of classic PIDs and their difficulty in tuning in complex situations is explained through their connection with iPIDs. Numerical simulations, including infinite-dimensional systems, demonstrate the power and simplicity of intelligent controllers. Keywords: Model-free control, PID controllers, intelligent PID controllers, intelligent PI controllers, intelligent P controllers, estimation, noise, flatness-based control, delay systems, non-minimum phase systems, fault accommodation, heat partial differential equations, operational calculus, functional analysis, differential algebra. The paper presents the general principles of model-free control and corresponding intelligent PIDs. It discusses the online estimation of the crucial term F, explains why friction allows intelligent PIDs to be used for proportional or proportional-integral correctors, and presents numerical simulations for various case-studies. These include systems with partially known components, robustness to system changes, nonlinear systems, delay systems, and a one-dimensional semi-linear heat equation. The paper also explains the industrial capabilities of classic PIDs by relating them to intelligent controllers. It concludes with a discussion of open problems and the potential impact of model-free control on the development of automatic control.Model-free control, along with intelligent PID controllers (iPIDs), is introduced in this paper for the first time in an unified manner, incorporating recent advances. The basics of model-free control utilize old functional analysis and elementary differential algebra. Estimation techniques are straightforward via recent online parameter identification methods. The importance of iPIDs, especially iPs, is derived from the presence of friction. The widespread use of classic PIDs and their difficulty in tuning in complex situations is explained through their connection with iPIDs. Numerical simulations, including infinite-dimensional systems, demonstrate the power and simplicity of intelligent controllers. Keywords: Model-free control, PID controllers, intelligent PID controllers, intelligent PI controllers, intelligent P controllers, estimation, noise, flatness-based control, delay systems, non-minimum phase systems, fault accommodation, heat partial differential equations, operational calculus, functional analysis, differential algebra. The paper presents the general principles of model-free control and corresponding intelligent PIDs. It discusses the online estimation of the crucial term F, explains why friction allows intelligent PIDs to be used for proportional or proportional-integral correctors, and presents numerical simulations for various case-studies. These include systems with partially known components, robustness to system changes, nonlinear systems, delay systems, and a one-dimensional semi-linear heat equation. The paper also explains the industrial capabilities of classic PIDs by relating them to intelligent controllers. It concludes with a discussion of open problems and the potential impact of model-free control on the development of automatic control.