Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs
This provides a simpler alternative to traditional control methods for engineers, though it appears incremental in approach.
The paper tackles model-free predictive control by reformulating it as a linear differential equation with constant coefficients, eliminating the need for complex modeling. Results show comparable performance to model-based approaches with lower computational burden.
Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thanks to a new perspective on optimal control combined with recent advances in the field of model-free control (MFC). It is replacing Dynamic Programming, the Hamilton-Jacobi-Bellman equation, and Pontryagin's Maximum Principle. The computing burden is low. The implementation is straightforward. Two nonlinear examples, a chemical reactor and a two tank system, are illustrating our approach. A comparison with the HEOL setting, where some expertise of the process model is needed, shows only a slight superiority of the later. A recent identification of the two tank system via a complex ANN architecture might indicate that a full modeling and the corresponding machine learning mechanism are not always necessary neither in control, nor, more generally, in AI.