This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, filter coefficients, the sliding surface of a sliding mode controller, or weights of a neural network. Hence, the proposed method can be applied to a wide range of controllers. The method uses a Kalman filter that estimates control parameters, using data of closed-loop system operation. The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system. The performance-driven calibration method tunes the parameters online and robustly, is computationally efficient, has low data storage requirements, and is easy to implement making it appealing for many real-time applications. Simulation results show that the method is able to learn control parameters quickly, is able to tune the parameters to compensate for disturbances, and is robust to noise. A simulation study with the high-fidelity vehicle simulator CarSim shows that the method can calibrate controllers of a complex dynamical system online, which indicates its applicability to a real-world system. We also verify the real-time feasibility on an embedded platform with automotive-grade processors by implementing our method on a dSPACE MicroAutoBox-II rapid prototyping unit.

翻译：本文提出一种控制参数校准方法。此类控制参数包括PID控制器增益、最优控制代价函数权重、滤波器系数、滑模控制器滑动面参数或神经网络权重等。因此，所提方法可广泛应用于各类控制器。该方法利用卡尔曼滤波器基于闭环系统运行数据估计控制参数。控制参数校准由训练目标驱动，该目标包含对动态系统性能的规格要求。这种性能驱动的校准方法可在线鲁棒地调节参数，具有计算效率高、数据存储需求低、易于实现等优势，使其适用于诸多实时应用场景。仿真结果表明，该方法能快速学习控制参数，可有效调节参数以补偿干扰，并具有良好的噪声鲁棒性。基于高保真车辆仿真器CarSim的仿真研究显示，该方法能在复杂动态系统上在线校准控制器，表明其具备应用于实际系统的潜力。我们还将该方法部署于dSPACE MicroAutoBox-II快速原型验证单元，在配备汽车级处理器的嵌入式平台上验证了其实时可行性。