- The paper introduces an innovative control strategy that achieves uniform exponential stability for N-DOF manipulators under actuator faults and torque constraints.
- It integrates a robust subsystem-based adaptive method with a modified JAYA algorithm to efficiently fine-tune control gains.
- Simulation results demonstrate precise trajectory tracking and enhanced system resilience even in the presence of significant actuator degradations.
Introduction
The field of robotics has seen substantial advancements in developing control systems for manipulators that play a vital role in various industries. Challenges often arise due to the complexity of robotic systems, especially those with multiple degrees of freedom (DOF), as they must execute precise movements while being robust against potential hardware faults and input limitations. This paper details an innovative solution to enhance the control and stability of n-DOF robot manipulators while adhering to torque constraints and compensating for actuator faults and system uncertainties.
System Modeling and Fault Management
The paper outlines a mathematical model of a robot manipulator, reflecting the influences of inertia, centrifugal and Coriolis forces, as well as gravity and external disturbances. A critical aspect of this model is the introduction of a fault model that accommodates various actuator issues, ranging from complete failures to performance degradations. Furthermore, the model integrates a mathematical saturation function to ensure the torques applied do not exceed predefined thresholds, safeguarding against excessive force that could jeopardize the manipulator's structural integrity or functionality.
Adaptive Control Strategy
To cope with the challenges posed by actuator faults and unknown system uncertainties, the paper proposes a robust subsystem-based adaptive control strategy. This innovative approach leverages subsystem isolation to manage the system's complexity and applies a robust adaptive method to guide the manipulator and align it with the desired trajectories. The strategy ensures uniform exponential stability, preserving desired performance levels despite faults or limitations in control inputs.
Optimization Algorithm Enhancement
Crucial to the success of this fault-tolerant control system is the optimization of controller gains. By tuning these parameters, the system's responsiveness and stability can be significantly enhanced. To achieve this, the paper amends the JAYA algorithm, a powerful swarm intelligence technique, removing the need for algorithm-specific parameter fine-tuning and improving the algorithm's efficiency in seeking optimal solutions. The paper demonstrates this method's superiority over traditional optimization algorithms in terms of precision and convergence speed.
Simulation Results and Analysis
The paper showcases the validity of the proposed control solution through simulation results. These results underline the system's proficiency in maintaining stability and control, even when faced with various actuator faults. The adaptive control strategy's performance, supported by the optimized JAYA algorithm, illustrates a clear capacity for adjusting to different types of actuator issues quickly and effectively, achieving precise trajectory tracking with minimal deviations.
Conclusion
The research introduces a fault-tolerant control system specifically tailored for high-DOF robotic manipulators, characterized by its ability to ensure uniform exponential stability amid actuator failures and input constraints. The control system adapts to faults and uncertainties by optimizing control gains through an enhanced swarm intelligence algorithm, ensuring high precision and swift adaptation to dynamic changes. This system not only paves the way for further refinement in robotic control methods but also offers a robust approach that can be translated across various robotic platforms. The outcomes demonstrate a compelling improvement over existing methods, potentially leading to more reliable and efficient robotic operations in the future.