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;List of the main control techniques
*[[Adaptive control]] uses on-line identification of the process parameters, or modification of controller gains, thereby obtaining strong robustness properties. Adaptive controls were applied for the first time in the [[aerospace industry]] in the 1950s, and have found particular success in that field.▼
*A [[hierarchical control system]] is a type of [[control system]] in which a set of devices and governing software is arranged in a [[hierarchical]] [[tree (data structure)|tree]]. When the links in the tree are implemented by a [[computer network]], then that hierarchical control system is also a form of [[networked control system]].▼
*[[Intelligent control]] uses various AI computing approaches like [[artificial neural networks]], [[Bayesian probability]], [[fuzzy logic]],<ref>{{cite journal | title=A novel fuzzy framework for nonlinear system control| journal=Fuzzy Sets and Systems | year=2010 | last1=Liu |first1=Jie |author2=Wilson Wang |author3=Farid Golnaraghi |author4=Eric Kubica | volume=161 | issue=21 | pages=2746–2759 | doi=10.1016/j.fss.2010.04.009}}</ref> [[machine learning]], [[evolutionary computation]] and [[genetic algorithms]] or a combination of these methods, such as [[neuro-fuzzy]] algorithms, to control a [[dynamic system]].▼
*[[Optimal control]] is a particular control technique in which the control signal optimizes a certain "cost index": for example, in the case of a satellite, the jet thrusts needed to bring it to desired trajectory that consume the least amount of fuel. Two optimal control design methods have been widely used in industrial applications, as it has been shown they can guarantee closed-loop stability. These are [[Model Predictive Control]] (MPC) and [[linear-quadratic-Gaussian control]] (LQG). The first can more explicitly take into account constraints on the signals in the system, which is an important feature in many industrial processes. However, the "optimal control" structure in MPC is only a means to achieve such a result, as it does not optimize a true performance index of the closed-loop control system. Together with PID controllers, MPC systems are the most widely used control technique in [[process control]].
*[[Robust control]] deals explicitly with uncertainty in its approach to controller design. Controllers designed using ''robust control'' methods tend to be able to cope with small differences between the true system and the nominal model used for design.<ref>{{cite journal|last1=Melby|first1=Paul|last2=et.|first2=al.|title=Robustness of Adaptation in Controlled Self-Adjusting Chaotic Systems |journal=Fluctuation and Noise Letters |volume=02|issue=4|pages=L285–L292|date=2002|doi=10.1142/S0219477502000919}}</ref> The early methods of [[Hendrik Wade Bode|Bode]] and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness. Examples of modern robust control techniques include [[H-infinity loop-shaping]] developed by Duncan McFarlane and [[Keith Glover]], [[Sliding mode control]] (SMC) developed by [[Vadim Utkin]], and safe protocols designed for control of large heterogeneous populations of electric loads in Smart Power Grid applications.<ref name='TCL1'>{{cite journal|title=Safe Protocols for Generating Power Pulses with Heterogeneous Populations of Thermostatically Controlled Loads |author=N. A. Sinitsyn. S. Kundu, S. Backhaus |journal=[[Energy Conversion and Management]]|volume=67|year=2013|pages=297–308|arxiv=1211.0248|doi=10.1016/j.enconman.2012.11.021|s2cid=32067734 }}</ref> Robust methods aim to achieve robust performance and/or [[Stability theory|stability]] in the presence of small modeling errors.
*[[Stochastic control]] deals with control design with uncertainty in the model. In typical stochastic control problems, it is assumed that there exist random noise and disturbances in the model and the controller, and the control design must take into account these random deviations.
▲*[[Adaptive control]] uses on-line identification of the process parameters, or modification of controller gains, thereby obtaining strong robustness properties. Adaptive controls were applied for the first time in the [[aerospace industry]] in the 1950s, and have found particular success in that field.
▲*A [[hierarchical control system]] is a type of [[control system]] in which a set of devices and governing software is arranged in a [[hierarchical]] [[tree (data structure)|tree]]. When the links in the tree are implemented by a [[computer network]], then that hierarchical control system is also a form of [[networked control system]].
▲*[[Intelligent control]] uses various AI computing approaches like [[artificial neural networks]], [[Bayesian probability]], [[fuzzy logic]],<ref>{{cite journal | title=A novel fuzzy framework for nonlinear system control| journal=Fuzzy Sets and Systems | year=2010 | last1=Liu |first1=Jie |author2=Wilson Wang |author3=Farid Golnaraghi |author4=Eric Kubica | volume=161 | issue=21 | pages=2746–2759 | doi=10.1016/j.fss.2010.04.009}}</ref> [[machine learning]], [[evolutionary computation]] and [[genetic algorithms]] or a combination of these methods, such as [[neuro-fuzzy]] algorithms, to control a [[dynamic system]].
*[[Self-organized criticality control]] may be defined as attempts to interfere in the processes by which the [[self-organized]] system dissipates energy.
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