Nearly optimal fault-tolerant constrained tracking for multi-axis servo system via practical terminal sliding mode
Hanlin Dong1, Zhaoke Ning2, Zhiqiang Ma3
1School of Automation, Northwestern Polytechnical University, Xi'an, 710129, China.
ISA Transactions
|December 5, 2023
Summary
This study introduces a novel control strategy for robotic manipulators, enhancing tracking accuracy despite uncertainties and failures. The method ensures robust performance by integrating adaptive dynamic programming and neural networks for precise robotic control.
Area of Science:
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background:
- Robotic manipulators face challenges like parameter uncertainties, actuator faults, and input saturation.
- Existing control methods may struggle with complex, real-world operational constraints.
Purpose of the Study:
- To develop a nearly optimal tracking control for n-links robotic manipulators.
- To address parameter uncertainties, time-profile failures, and input saturation constraints effectively.
Main Methods:
- Design of a practical terminal sliding-mode (PTSM) manifold with a linear term.
- Development of a nearly optimal sliding-mode reaching law using adaptive dynamic programming (ADP).
- Integration of a radial basis function neural network (RBFNN) for fault and saturation compensation.
Main Results:
- The PTSM manifold ensures rapid convergence of controlled states to the equilibrium.
- The ADP-based reaching law constrains system dynamics to a desired region.
- RBFNN, updated by ADP's critic network, effectively compensates for actuator faults and input saturation.
Conclusions:
- The proposed control strategy significantly improves the tracking performance and robustness of robotic manipulators.
- The integrated approach, combining PTSM, ADP, and RBFNN, offers a comprehensive solution for complex robotic control challenges.
Related Concept Videos
01:24One-Degree-of-Freedom System
491
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
491
01:29Relative Motion Analysis using Rotating Axes-Problem Solving
406
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Here, in order to determine the magnitude of velocity and acceleration for point...
406
01:25Relative Motion Analysis using Rotating Axes
469
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
469
01:07Time-Domain Interpretation of PD Control
119
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
119
01:19PID Controller
120
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
120
01:26Feedback control systems
316
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
316