Model Predictive Control (MPC) Approach for 3-Axis UAV Gimbals for Target Tracking

Model Predictive Control (MPC) Approach for 3-Axis UAV Gimbals for Target Tracking

The gimbal is the base for your drone camera, which ensures smooth video capture and high-quality photos.3-axis uav: electro mastery The gimbal is made from aluminium alloy to be sturdy and durable, while the damping structure absorbs vibrations during intensive flight manoeuvres. It is compatible with a variety of drones and can be mounted with SLR / mirrorless cameras for professional aerial shooting. It is also ideal for aerial mapping, surveying and inspection.

Drones are flying aircraft without an on-board pilot that perform a range of functions such as imaging, detecting, identifying, tracking and positioning.3-axis uav: electro mastery To ensure stable and accurate control of these drones, they are equipped with various sensors such as GPS, camera gimbal system, load transporting system, vertical take-off and landing (VTOL) system, flight control, image transmission, telemetry and laser marking modules [1].

Gimbal systems mounted on UAVs are important to stabilize the line of sight of the sensor for target tracking.3-axis uav: electro mastery The effective control of the gimbal system directly affects the performance of UAV target tracking. Hence, numerous studies focus on modeling and controlling the UAV to achieve better performance in this task.

Moreover, the control of the gimbal system is influenced by the external disturbances induced by operating environment, which makes the model-based control difficult to apply in practice.3-axis uav: electro mastery In order to address this issue, a new MPC controller is proposed for the three-axis gimbal system mounted on UAV to improve real time target tracking performance under external disturbances.

This paper aims to develop a model predictive control (MPC) approach based on nonlinear Hammerstein block structure to effectively control the three-axis gimbal used for UAV target tracking. First, the linear and nonlinear OE and Hammerstein models of the three-axis gimbal are identified and their control laws are derived. Then, an MPC controller with the identified OE and Hammerstein models is designed to guarantee precise tracking of the UAV for target tracking in the presence of external disturbances.

The experimental results of the proposed MPC controller demonstrate that it is able to track the target precisely and accurately while maintaining stability in the UAV. Furthermore, the performance of the UAV is improved by forty-three percent to fifty-seven percent compared with conventional fuzzy PID controller.

In addition, the MPC algorithm can be implemented in a real-time system, and the results of the simulations are comparable to those of the experiments. Therefore, the proposed MPC algorithm is promising for enhancing the real-time target tracking performance of the UAV based on a three-axis gimbal. It can be a useful tool for various applications including UV corona discharge detection of power lines, forest fire fighting and railway power, as well as the locating of underground oil leaks. It is also expected to enhance the performance of the UV gimbal camera for UAVs. Hence, it is worthy of further study and application in the future.