用可变参数动态模型提升机器人控制预测精度
Beyond Constant Parameters: Hyper Prediction Models and HyperMPC
- 用神经网络学习动态参数随时间变化规律
- 在真实赛车任务中将长时预测误差显著降低
- 适合需要高精度预测的自主机器人控制场景
模型预测控制（MPC）是机器人控制中最广泛使用且可靠的算法之一，其性能高度依赖于精确的动力学模型。然而，现有基于梯度的MPC所用动力学模型受限于计算复杂度和状态表示能力。为此，我们提出超预测模型（HyperPM），将未建模动态投影到时变动力学模型上，通过神经网络学习模型参数在MPC预测时域内的演化规律。该方法在保持基础模型计算效率与鲁棒性的同时，赋予其预测此前未建模现象的能力。我们在多个挑战性系统上进行了评估，包括真实的F1TENTH自动驾驶赛车任务，结果表明该方法显著降低了长时域预测误差。当集成至MPC框架（HyperMPC）后，本方法持续优于现有最先进技术。
原文摘要 · Abstract (English)
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this limitation, we propose the Hyper Prediction Model (HyperPM) - a novel approach in which we project the unmodeled dynamics onto a time-dependent dynamics model. This time-dependency is captured through time-varying model parameters, whose evolution over the MPC prediction horizon is learned using a neural network. Such formulation preserves the computational efficiency and robustness of the base model while equipping it with the capacity to anticipate previously unmodeled phenomena. We evaluated the proposed approach on several challenging systems, including real-world F1TENTH autonomous racing, and demonstrated that it significantly reduces long-horizon prediction errors. Moreover, when integrated within the MPC framework (HyperMPC), our method consistently outperforms existing state-of-the-art techniques.
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