![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/wall_clearance_challenge.png\"){kind=spacer}
<\/p>\nHere's your challenge. You're controlling a sliding gantry crane that is supporting a heavy swinging load. You need to pilot that load safely over a fragile wall. Can you do it? How?<\/p>\n
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I find this problem interesting because it's so dynamic and nonlinear. I can imagine how I would do it manually if I was driving the crane with a joystick. But I'm not sure how I would attack it as a MATLAB controls problem. In this post I work with Claude Code to create the scenario, solve the related controls problem, and then animate it using Blender. It was all remarkably straightforward.<\/p>\n
First I described the plant: gantry crane and pendulum. This is a four-state system where the input is the force driving the crane and the output is the x position of the pendulum bob. The initial challenge was to move the pendulum quickly from one position to another. Claude chose an LQR (Linear Quadratic Regulator) controller. I was happy with the result shown here.<\/p>\n
![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/crane_bob_control.gif\")
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Let's add the constraint: the pendulum has to get over a high wall. Claude solved the problem in two steps. There's an aggressive thrust-brake maneuver to get the pendulum high enough to clear the wall, followed by a \"catch\" phase that can safely fall back to the linear assumptions of the LQR controller, bringing things to a graceful conclusion. This overall plan works in theory, but it resulted in a system that looked unrealistic. In the real world, the gantry crane would never be able to move that fast. <\/p>\n
![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/crane_wall_clearance.gif\"){kind=spacer}
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Now we move on to the next constraint: what's the least control input we can get away with? I asked Claude to use a resonant pump to boost the pendulum with as little control effort as possible. It then ran a tuning loop until the maximum actuator force was reduced by a factor of 20. Once over the wall, the LQR would catch the pendulum as before. Here is the result. First the data.<\/p>\n
![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/resonant_pump_plot.png\")
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Now for the animation.<\/p>\n
![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/crane_resonant_pump.gif\")
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At this point, the technical problem has been solved. But if we really want to sell this idea, we need a better story. I wanted to try multi-modal storytelling with Claude driving both MATLAB and Blender<\/a>. It worked really well! Using the Blender's MCP connection, I could just say to Claude \"Take this simulation from MATLAB and use it to create a scene in Blender.\" It really was about that simple. <\/p>\n I made some improvements to the wall (make it a brick wall) and the gantry (add more detail). But it took very little work to get to this satisfying animation.<\/p>\n The simulation is exactly the same, but we are able to heighten the drama of scenario. Before AI, it wouldn't have been worth my time to build a visualization like this. But it's so easy now that I couldn't resist. The combination of MATLAB's accuracy and control design with Blender's scene rendering is hard to beat.<\/p>\n","protected":false},"excerpt":{"rendered":" Here's your challenge. You're controlling a sliding gantry crane that is supporting a heavy swinging load. You need to pilot that load safely over a fragile wall. Can you do it? How?<\/p>\n![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/blender_scene.png\")
<\/p>\n![\"\"](\"https:\/\/blogs.mathworks.com\/community\/files\/crane_swing.gif\")
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