An ANN-Enhanced Approach for Flatness-Based Constrained Control of Nonlinear Systems

Abstract

Neural networks have proven practical for a synergistic combination of advanced control techniques. This work analyzes the implementation of rectified linear unit neural networks to achieve constrained control in differentially flat systems. Specifically, the class of flat systems enjoys the benefit of feedback linearizability, i.e., the systems can be linearized by means of a proper variable transformation. However, the price for linearizing the dynamics is that the constraint descriptions are distorted geometrically. Our results show that, by using neural networks, these constraints can be represented as a union of polytopes, enabling the use of mixed-integer programming tools to guarantee constraint satisfaction. We further analyze the integration of the characterization into efficient settings such as control Lyapunov function-based and model predictive control (MPC). Interestingly, this description also allows us to explicitly compute the solution of the MPC problem for the nonlinear system. Several examples are provided to illustrate the effectiveness of our framework.

References in corpus (5)

• Efficient representation and approximation of model predictive control laws via deep learning
• Data-based Control of Feedback Linearizable Systems
• A flatness-based predictive controller for six-degrees of freedom spacecraft rendezvous
• Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filter
• Robust stabilization of polytopic systems via fast and reliable neural network-based approximations