Foreword
In recent years, with the development of robotics and control technology, robots have been widely used in daily life and industrial and agricultural production. A robot is a nonlinear, strongly coupled, multivariable system, and during its motion, due to uncertainties such as friction and load changes, it is also a time-varying system. Traditional robot control technologies are mostly model-based control methods, which cannot achieve satisfactory trajectory tracking results. The development of artificial intelligence, such as fuzzy control and neural networks, has provided new ideas for solving the robot trajectory tracking problem. The control rules of ordinary fuzzy control are mostly summaries of human experience. They lack self-learning and adaptive capabilities and are often influenced by human subjectivity. Therefore, they cannot effectively control time-varying and uncertain systems.
In recent decades, fuzzy systems based on fuzzy logic have become a very active field. Some algorithms have shown considerable capability in the design of controllers for complex systems, and fuzzy mathematics theory has also provided an extremely superior tool for constructing knowledge models.
Because neural networks possess excellent self-learning, self-adaptation, and associative intelligence, they can adapt to the complex and ever-changing dynamic characteristics of systems. The combination of fuzzy control and neural networks has become a key research focus. This research originated in Europe and America, but achieved significant development in Japan in the late 1980s. Currently, in the field of knowledge and information processing, it has reached a unique research stage, independent of fuzzy logic and neural network technologies. The integration of fuzzy and neural network technologies overcomes the shortcomings of neural networks and fuzzy logic in knowledge processing, possessing functions such as supervised learning, processing experiential knowledge, and online learning based on language expression. The nonlinear mapping and self-learning capabilities of neural networks are used to adjust fuzzy control, giving it a certain degree of adaptability while also enabling the neural network to acquire the reasoning and inductive capabilities of fuzzy control. This paper studies the application of fuzzy neural networks in robot control and proposes a fuzzy neural network-based robot trajectory tracking control. Simulation results show that this control method can effectively track the robot trajectory.
1. Establishment of robot control system
In this system, the stereo positioning system serves as the primary data input channel, accurately acquiring the precise relative position between the target location and the robot. This real-time spatial information is then integrated into the previously established spatial model. During this process, it is necessary to determine the transformation relationship between the previous model and the actual 3D space, i.e., registration.
Then, the robot performs motion operations according to the motion plan formulated by the computer-aided system. During the motion, the stereo positioning system continuously collects the spatial position of the robot relative to the target, and performs visual control in conjunction with the robot's multi-axis controller. The robot control system is shown in Figure 1. In the block diagram, the input is the feedback current of the robot's walking drive servo motor, and the output is the robot's walking speed, which is achieved by servo speed regulation.
Figure 1 Robot Control System
This paper designs a six-degree-of-freedom (DOF) robot: three rotational and three translational. The robot's six DEFs work together to complete spatial motion. Considering the robot's small size, the goal is to minimize weight. This necessitates limiting the overall load on the mechanism due to reduced stiffness, while also considering stability during high-speed motion. Furthermore, the stiffness design of this multi-DOF mechanism depends on the speed and direction of motion.
2. Algorithm for Direct Neural Controller
4. System simulation research
To verify the effectiveness of the proposed neural network control algorithm, a neural network was created in MATLAB. The abstract fuzzy rules were transformed into training samples for the fuzzy neural network using membership functions and fuzzy rules. The hidden layer used the Tansig function, which is differentiable at any point, as the transfer function, and the output layer used the commonly used non-negative Sigmoid function.
The response curves of the system to a step signal when using conventional PID control and neural network control are shown in Figure 3. Figure 3 shows the error response curves of the conventional PID controller and the fuzzy neural network controller for tracking a sinusoidal signal. By comparison, it can be seen that the neural network controller is significantly better than the conventional PID controller in terms of dynamic performance, and can reduce the sinusoidal response error from 0.02 rad to 0.001 rad.
5. Conclusion
Traditional, simple direct neural controllers can be easily implemented, saving significant CPU time. This paper combines fuzzy control with neural networks to design a neural network-based robot servo positioning control system, which is then applied to a robot trajectory tracking control system. Neural control with well-trained parameters can improve adaptive capability and enhance the control performance of nonlinear control systems. Simulation results demonstrate that this control system effectively overcomes the effects of nonlinearity and coupling in robot systems, making it a promising control method.
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