Robust Non-Overshooting Sliding Mode Control for UAV Trajectory Tracking

Core Concepts

A robust non-overshooting sliding mode control method is presented to achieve global exponential stability and precise trajectory tracking for UAVs, even in the presence of bounded stochastic disturbances and time-varying reference trajectories.

Abstract

The paper proposes a robust non-overshooting sliding mode control method for a class of uncertain systems, with a focus on UAV trajectory tracking applications. The key highlights are:

The control design is based on a global non-overshooting 2-sliding mode system, which consists of two successive subsystems.

The first subsystem with non-overshooting reachability compresses the initial values of the sliding variables to a bounded range, enabling the second subsystem to have bounded system gains and achieve local non-overshooting stability.

Through partitioning the initial values of the sliding variables, the bounded system parameters are analytically determined to satisfy the robust non-overshooting stability.

The sliding mode gains can completely reject the influence of bounded stochastic disturbances or uncertainties, without any requirement on the disturbance derivatives.

The proposed method can achieve global exponential stability and precise tracking of time-varying reference trajectories without overshoot, even when the reference suddenly changes or jumps.

To reduce chattering in the controller output, a tanh-function-based smoothed non-overshooting sliding mode is developed, which provides a continuous and bounded control law.

The effectiveness of the proposed non-overshooting sliding mode control is demonstrated through UAV flight tests, considering adverse conditions such as model uncertainties, bounded disturbances, and noisy measurements.

Stats

The bounded unknown disturbance d(t) satisfies supt∈[0,∞) |d(t)| ≤Ld < ∞, where Ld = 5.
The system gain k2 is bounded by k2M = 20.

Quotes

"Non-overshooting stability requires that the system gain depends on the initial values of the system variables, making the system locally stable."
"In order to achieve the global non-overshooting stability and avoid the excessively large system gains, the 2-sliding mode consists of two bounded-gain subsystems."
"The sliding mode gains can be assigned to completely eliminate the influence of bounded stochastic disturbance."

Key Insights Distilled From

Non-overshooting sliding mode for UAV control

by Xinhua Wang,... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01087.pdf

Deeper Inquiries

How can the proposed non-overshooting sliding mode control be extended to handle more complex UAV dynamics, such as those involving aerodynamic nonlinearities or actuator constraints

The proposed non-overshooting sliding mode control can be extended to handle more complex UAV dynamics by incorporating aerodynamic nonlinearities and actuator constraints into the control design.

Aerodynamic Nonlinearities: To address aerodynamic nonlinearities, the control algorithm can be augmented with adaptive control techniques that can adjust the control parameters based on the varying aerodynamic conditions. Nonlinear control strategies such as feedback linearization or backstepping can be employed to handle the nonlinear dynamics of the UAV's aerodynamics. By modeling the aerodynamic forces and moments accurately and incorporating them into the control law, the system can effectively compensate for these nonlinear effects.

Actuator Constraints: When dealing with actuator constraints, the control algorithm needs to consider the limitations of the actuators in terms of their maximum deflection angles, rates, or saturation limits. By implementing anti-windup techniques or model predictive control, the control system can ensure that the control inputs remain within the feasible range of the actuators. Additionally, the control design can incorporate constraints directly into the optimization problem to ensure that the control signals do not violate the actuator limits.

By integrating these considerations into the non-overshooting sliding mode control framework, the UAV system can effectively handle the complexities introduced by aerodynamic nonlinearities and actuator constraints, ensuring stable and precise control even in challenging operating conditions.

What are the potential limitations of the current approach, and how could it be further improved to enhance the robustness and adaptability for a wider range of UAV applications

The current approach may have some limitations that could be addressed to further enhance its robustness and adaptability for a wider range of UAV applications:

Sensitivity to Model Uncertainties: The control system's performance may be affected by uncertainties in the UAV dynamics or environmental conditions. Implementing robust control techniques, such as H-infinity control or adaptive control, can improve the system's resilience to uncertainties and variations.

Real-time Adaptation: The control algorithm may benefit from real-time adaptation capabilities to adjust to changing operating conditions or mission requirements. Incorporating online parameter tuning or reinforcement learning algorithms can enhance the system's adaptability and performance.

Integration of Machine Learning: Leveraging machine learning algorithms for system identification, adaptive control, or anomaly detection can further improve the control system's capabilities. By learning from data and experiences, the control system can optimize its performance and adapt to new scenarios.

By addressing these limitations and incorporating advanced control strategies, the non-overshooting sliding mode control can be further improved to meet the demands of diverse UAV applications, enhancing its robustness and adaptability.

Given the focus on trajectory tracking, how could the non-overshooting sliding mode control be integrated with higher-level mission planning and decision-making algorithms to enable more autonomous and intelligent UAV operations

To integrate the non-overshooting sliding mode control with higher-level mission planning and decision-making algorithms for more autonomous and intelligent UAV operations, the following approaches can be considered:

Hierarchical Control Architecture: Implement a hierarchical control structure where the non-overshooting sliding mode control operates at the lower level for trajectory tracking, while higher-level controllers manage mission planning, obstacle avoidance, and decision-making. Communication and coordination between the different control layers can ensure seamless operation and goal achievement.

Multi-Agent Systems: Utilize multi-agent systems where each UAV is equipped with the non-overshooting sliding mode control for individual trajectory tracking, while a centralized or distributed decision-making algorithm coordinates multiple UAVs for collaborative missions. This approach enables autonomous swarm operations with efficient task allocation and coordination.

Reinforcement Learning: Integrate reinforcement learning algorithms to enable the UAV to learn optimal control policies based on rewards and penalties. By training the UAV in simulation or real-world environments, it can adapt its control strategy to achieve mission objectives while adhering to non-overshooting constraints.

By combining the non-overshooting sliding mode control with advanced planning and decision-making algorithms, UAVs can operate autonomously, make intelligent decisions, and execute complex missions with precision and efficiency.