The three main types of control systems are linear, nonlinear and decentralised. Each one of them must first guarantee the stability of the closed-loop behaviour. The method differs depending on the type of control system used. In the case of linear systems, it is obtained by directly placing the poles. Non-linear systems use the stability theory (The qualitative behaviour of an orbit under perturbations which can be analysed using the linearisation of the system near the orbit. If all eigenvalues are negative real numbers or complex numbers then the point is a stable attracting fixed one. If none of the eigenvalues is imaginary or zero, then the attracting directions are related to the eignespaces of the matrix). A list of the main control techniques includes: Adaptive control, hierarchical control, intelligent control, optimal control, robust control or stochastic control.

Adaptive control was first discovered in 1950s and found particular success in aerospace industry. It uses on-line identification of parameters and obtains strong robustness proprieties.

Hierarchical control is the type in which the devices and software are arranged in a hierarchical tree.

The intelligent control uses various computing approaches like neural networks, machine learning or genetic algorithms in order to control the dynamic system.

Optimal control techniques are particularly built to function in order to optimise cost indexes. The two main methods used are the Model Predictive Control and Linear-Quadratic-Gaussian control. These are widely used in industrial application, have been shown to generate closed-loop stability and are the more widely used process control techniques.

Robust methods aim to achieve robust performance and stability even in the case of small modelling errors. They deal specifically with uncertainty in the case of controller design.

Stochastic control assumes that there is random noise and disturbances in the model and the controller and takes them into account as generating stability.

Adaptive control was first discovered in 1950s and found particular success in aerospace industry. It uses on-line identification of parameters and obtains strong robustness proprieties.

Hierarchical control is the type in which the devices and software are arranged in a hierarchical tree.

The intelligent control uses various computing approaches like neural networks, machine learning or genetic algorithms in order to control the dynamic system.

Optimal control techniques are particularly built to function in order to optimise cost indexes. The two main methods used are the Model Predictive Control and Linear-Quadratic-Gaussian control. These are widely used in industrial application, have been shown to generate closed-loop stability and are the more widely used process control techniques.

Robust methods aim to achieve robust performance and stability even in the case of small modelling errors. They deal specifically with uncertainty in the case of controller design.

Stochastic control assumes that there is random noise and disturbances in the model and the controller and takes them into account as generating stability.