Machine learning control (MLC) is a subfield of

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

intelligent control Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and geneti ...

and

control theory Control theory is a field of mathematics that deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a ...

which solves optimal control problems with methods of

. Key applications are complex nonlinear systems for which

linear control theory A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial co ...

methods are not applicable.

Types of problems and tasks

Four types of problems are commonly encountered. * Control parameter identification: MLC translates to a parameter identificationThomas Bäck & Hans-Paul Schwefel (Spring 1993
"An overview of evolutionary algorithms for parameter optimization"
Journal of Evolutionary Computation (MIT Press), vol. 1, no. 1, pp. 1-23 if the structure of the control law is given but the parameters are unknown. One example is the genetic algorithm for optimizing coefficients of a

PID controller A proportional–integral–derivative controller (PID controller or three-term controller) is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuou ...

N. Benard, J. Pons-Prats, J. Periaux, G. Bugeda, J.-P. Bonnet & E. Moreau, (2015
"Multi-Input Genetic Algorithm for Experimental Optimization of the Reattachment Downstream of a Backward-Facing Step with Surface Plasma Actuator"
Paper AIAA 2015-2957 at 46th AIAA Plasmadynamics and Lasers Conference, Dallas, TX, USA, pp. 1-23. or discrete-time optimal control. * Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known full state feedback. A neural network is commonly used technique for this task. * Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the cost function of the plant. In this case, neither a model, nor the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant.

Genetic programming In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to t ...

is a powerful regression technique for this purpose. * Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

. MLC comprises, for instance, neural network control, genetic algorithm based control, genetic programming control, reinforcement learning control, and has methodological overlaps with other data-driven control, like

artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech r ...

and

robot control Robotic control is the system that contributes to the movement of robots. This involves the mechanical aspects and programmable systems that makes it possible to control robots. Robotics could be controlled in various ways, which includes using ma ...

Applications

MLC has been successfully applied to many nonlinear control problems, exploring unknown and often unexpected actuation mechanisms. Example applications include * Attitude control of satellites. * Building thermal control. * Feedback turbulence control. * Remotely operated under water vehicle. * Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).Peter J. Fleming, R. C. Purshouse (200
"Evolutionary algorithms in control systems engineering: a survey"
Control Engineering Practice, vol. 10, no. 11, pp. 1223-1241 As for all general nonlinear methods, MLC comes with no guaranteed convergence, optimality or robustness for a range of operating conditions.

Types of problems and tasks

Applications

References

Further reading