Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves

optimal control Optimal control theory is a branch of mathematical optimization that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in science, engineering and ...

problems with methods of machine learning. Key applications are complex nonlinear systems for which linear control theory 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 In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...

for optimizing coefficients of a PID controllerN. 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 A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...

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 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. 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 and robot control.

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