Robust robotic control: EKF and CF-enhanced PID framework

This framework tackles two common problems in industrial robot arms: following a desired path accurately and rejecting disturbances. Ordinary proportional‑integral‑derivative (PID) controllers often lose performance when the robot faces changing loads, sensor noise, or other uncertainties. To overcome these issues, the project combines a robust control design with two filters: an Extended Kalman Filter (EKF) and a Complementary Filter (CF). * The EKF handles non‑linear state estimation. It removes high‑frequency noise from joint encoders and inertial sensors. * The CF merges data from several sensors to give fast, low‑latency estimates of orientation and velocity. The filtered outputs are used to adjust the PID gains in real time, creating an adaptive

feedback loop. The approach is tested in detailed simulations of multi‑degree‑of‑freedom robot arms. Results are compared with a standard PID controller and with setups that use only one of the filters. The key performance measures are: * smaller tracking error, * shorter settling time, and * better robustness when the payload changes. The guide also includes step‑by‑step instructions for building the robot model, designing the sensor‑fusion filters, and tuning the controller. This makes it easier for researchers to explore robust robotic automation.

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