从系列:理解模型预测控制
Melda Ulusoy, MathWorks
在本视频中,您将学习如何设计一个自适应MPC控制器的自动转向车辆系统的动力学变化与纵向速度。
在你为你的控制系统的最可能的操作条件设计一个MPC控制器之后,你可以实现一个基于该设计的自适应MPC控制器。在每个时间步中,自适应MPC更新工厂模型和当前运行条件的标称条件。在本视频中,您将学习如何计算和更新自适应MPC块所需的离散植物模型。您还将学习如何从您的自适应MPC控制器生成代码,您将看到一个例子,显示一个真正的自动驾驶汽车,使用MPC控制和图像处理算法,以保持自身在其车道内。
在该视频中,我们将使用自适应MPC自主地引导横向车辆动态随时间随时间变化而导致的汽车的速度。在以前的视频中,我们讨论了线性横向车辆动力学,并假设汽车具有恒定的纵向速度。因此,植物动态没有改变,州矩阵A是恒定的。要控制此系统,我们使用传统的MPC控制器。但现在我们将让纵向速度随着汽车旅行而变化。因此,状态矩阵A也将变化。传统的MPC控制器在处理不同的内部工厂模型时无效地处理不同的动态。那么,我们如何处理不断变化的植物动态?在第4件视频中,我们讨论了Adaptive MPC,当操作条件的变化时,Adaptive MPC允许您在每次步骤提供新的线性工厂模型,因此在新的操作条件下使预测更加准确。因此,要处理改变的植物动态,我们将使用Adaptive MPC。
我们打开一个新的Simuli万博1manbetxnk模型,从这个自定义库中添加工厂开始。在之前的视频中,目标被发展成一个状态空间系统它的输入是转向角输出是横向位置和偏航角。这一次,它的动力学随着纵向速度的变化而变化。因此,这现在成为植物块的输入。我们要连接一个恒定的纵速块,我们一开始设为15m /s,然后改变另一个值。另一个输出是我们稍后将使用的状态。如果你想查看模块下面是如何构建的,你可以从视频描述中给出的链接下载这个Simulink模型。万博1manbetx接下来,我们将连接模型预测控制工具箱下的自适应MPC块。这个块具有与常规MPC块相同的输入和输出,除了它还采用在当前运行条件的每个时间步更新的工厂模型。以前,我们设计了一个自定义的参考横向位置和偏航角。 We’ll first connect this reference to the controller. Then we connect the plant output to the measured outputs and the steering angle to the controller output. To implement the adaptive MPC, we can simply start with the MPC controller that we designed in the previous video for a longitudinal velocity of 15 m/s. We already have the MPC controller object in our workspace. By typing it in the command window, we can see the design parameters such as the prediction and control horizons, constraints and weights. One thing to note is that the adaptive MPC block requires a discrete plant model. So, we need to convert the continuous time state space model used by mpc1 to discrete time. There are different ways to do it. Here, we use the c2d command and update the plant model of the MPC object with the discretized plant. Now, we go back to the adaptive MPC block and type in the MPC object. Next, we need to provide the controller with a plant model that is updated at each time step for the current operating condition. The pre-built update plant model block takes care of this calculation. When we double click on it, we see that it has been implemented as a MATLAB function. As inputs, this function takes Vx, u and x and first calculates the state space matrices. It then computes the discrete model and also updates the nominal conditions with the current operating conditions. Now it’s time to connect the inputs and outputs for this block. We already have all the inputs here, longitudinal velocity, the steering angle and the states. The “model” input of the adaptive MPC control block requires the discrete-time model and nominal conditions in this order that we’ve created in the MATLAB function. To connect the outputs to the controller, we select the block, and create a bus signal. Now, we’re ready to try different longitudinal velocities and see how the controller handles the varying plant dynamics. In the previous video, the traditional MPC controller designed for an operating condition of 15 m/s had worked well while it failed to control the system at a different longitudinal velocity of 35 m/s. With adaptive MPC, we get a good controller performance when longitudinal velocity is 15 m/s. If we now change it to 35 m/s, we still get a good tracking of the lateral position and the yaw angle. We can even replace this constant block with a continuously changing signal such a sine wave and see that adaptive MPC still can deal with the changing plant dynamics and successfully control the system. We designed an adaptive MPC controller, ran several simulations to evaluate the controller performance. Now if you want to run your controller on your autonomous car, you can simply generate code using Embedded Coder and deploy it to your car. Here’s the generated C code. You can call the MPC controller code from your real-time scheduler using the entry points shown in the code interface report. Embedded Coder also lets you customize the call interfaces as required by your software framework and architecture.
这段视频展示了如何为MPC控制器生成代码,以及如何在自动驾驶汽车上运行图像处理算法,以使其保持在车道内。在Simulink之外开发的图像处理和车道检测算法,为MPC控制器提供这些输入。万博1manbetx以下是这些算法是如何工作的。汽车的正面视图是由安装在汽车顶部的摄像头捕获的。图像处理算法识别实线和虚线标记,并检测汽车行驶的车道。车道的中间是中心线,它用来计算汽车的位置与该线的偏移量以及偏航角。这个信息被MPC控制器用来使汽车保持在中心线上。左边的图显示汽车偏离中心线与红色和偏航角与绿色,而在右边的图显示转向角。
在这个视频中,我们讨论了如何使用自适应MPC来控制动态变化的工厂,也讨论了如何生成C代码并部署它进行实时控制。想了解更多关于模型预测控制的信息,请查看我们之前的Tech Talk视频。
你也可以从以下列表中选择一个网站:
请选择表现最佳的中国网站(中文或英文)。MathWorks的其他国家网站并没有针对您所在位置的访问进行优化。