Data underlying the PhD thesis: Learning-based control under constraints: Towards safety and computational efficiency

This dataset supports the PhD thesis titled <em>"Learning-Based Control Under Constraints: Towards Safety and Computational Efficiency"</em>. The thesis comprises six main chapters (Chapters 2–7), and the data is organized accordingly. All simulations were conducted in a MATLAB environment.<strong>Chapters 2–3</strong> focus on constrained approximate dynamic programming (ADP) algorithms for safe control. In particular, Chapter 2 presents a convex piecewise quadratic optimization algorithm designed to efficiently solve the ADP problem. Chapter 3 uses penalty methods to deal with constraints.<strong>Chapter 4</strong> includes code for the explicit approximation of safety filters.<strong>Chapter 5</strong> implements all-element predictive safety filters to ensure the safe control of piecewise affine (PWA) systems.<strong>Chapters 6–7</strong> propose an integrated optimization- and learning-based control framework for general constrained nonlinear systems.<br>

本数据集支撑题为《基于学习的约束控制：迈向安全性与计算效率》的博士学位论文。该论文包含6个核心章节（第2章至第7章），数据集亦据此进行组织。所有仿真实验均在MATLAB环境中开展。
第2-3章聚焦面向安全控制的约束近似动态规划（ADP，Approximate Dynamic Programming）算法。其中，第2章提出一种凸分段二次优化算法，用于高效求解近似动态规划问题；第3章采用惩罚方法处理约束条件。
第4章包含安全滤波器显式近似的相关代码。
第5章实现全元素预测安全滤波器，以保障分段仿射（PWA，Piecewise Affine）系统的安全控制。
第6-7章针对一般约束非线性系统，提出一种集成优化与学习的控制框架。