用凸优化解决控制不确定性问题，让系统更稳更可靠。
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
- 构建凸的随机约束控制框架，同时优化输入与风险分配
- 保证约束满足概率，且解唯一连续，计算更稳定
- 可应用于机器学习识别的非线性系统，适合新能源车控制
本文提出一种严格凸的随机约束控制框架，考虑参考轨迹和运行约束等控制规格中的不确定性。通过在一般（可能非高斯）不确定性下联合优化控制输入与风险分配，该方法在保证概率约束满足的同时，确保严格的凸性，从而实现最优解的唯一性和连续性。该框架进一步扩展至基于精确可线性化模型的非线性模型预测控制，这些模型通过机器学习方法识别得到。所提方法在混合动力传动系统模型预测控制中得到了验证，展示了其有效性。
原文摘要 · Abstract (English)
This paper presents a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and operational constraints. By jointly optimizing control inputs and risk allocation under general (possibly non-Gaussian) uncertainties, the proposed method guarantees probabilistic constraint satisfaction while ensuring strict convexity, leading to uniqueness and continuity of the optimal solution. The formulation is further extended to nonlinear model-based control using exactly linearizable models identified through machine learning. The effectiveness of the proposed approach is demonstrated through model predictive control applied to a hybrid powertrain system.
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