Constraint-Guided Data-Driven Learning Models for High-Dimensional Nonlinear Control Systems

Abstract

High-dimensional control systems Nonlinear control systems: Nonlinear control systems of high dimension naturally occur in very diverse modern engineering applications, such as autonomous robotics, smart power grids, high-tech transportation systems, and cyber-physical infrastructure on a large scale. Even though data-driven methods of learning like deep neural networks and reinforcement learning have shown effective functionality in approximating functions and have provided robust adaptability, they do not inherently and practically tackle control problems due to lack of stability assurance, common provision of safety and operational limits, and the incapacity to generalise the training domain. In order to overcome those, the paper will introduce a new constraint-directed data-driven learning framework to high dimensional nonlinear control systems that introduce the principles of physical laws and safety constraints, as well as control theory, into the learning process in a systematic manner. The suggested approach imposes state, input, and dynamical constraints directly into network structures and optimization goals such that the control policies that are learned are physically and operationally valid. Moreover, physics-based regularization terms are also added, so as to be consistent with known or partially known system dynamics, and Lyapunov-based regularization of stability is also proposed, so as to achieve a choice relating to closed-loop stability without necessarily designing any particular analytical controls. The resultant learning goal consolidates the optimization of the performance of a task, enforcement of constraints, physical consistency, and stability consciousness in one end-to-end training environment. Simulation experiments on the characteristic high-dimensional nonlinear benchmark systems have shown extensive simulation studies show that the approach used does everywhere outperform unconstrained neural and reinforcement learning-based controllers. The findings indicate that there are great improvements in trajectory tracking precision, full removal of constraint violation, increased strength to disturbance, and steady close-loop conduct in a comprehensive variety of operating situations. These results point to the efficacy of constraint-guided learning in narrowing the divide between data-driven intelligence and quality nonlinear control, providing an expanse of control application scaling and trustworthy to safety-critical and real-world control functions.