Application of Neural Networks in Control System

Application of Neural Networks in Control System

1. A method to use neural networks to control highly nonlinear system- "Neural

Networks for Self-Learning Control Systems." Feed-through, multilayered neural networks are used, and learning, via the back-propagation algorithm, is implemented to determine the neural network weights to first model the plant and then design the controller. First, a neural network emulator learns to identify the dynamic characteristics of the system. The controller, another multilayered network, then learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning continues as the emulator and controller improve as they track the physical process. The power of this approach is demonstrated by using the method to steer a trailer truck while backing up to a loading dock.

2. The control of mobile robots is the topic addressed - "Mobile Robot Control by a structured Hierarchical Neural Network." Neural networks are used to process data from many sensors for the real-time control of mobile robots and to provide the necessary learning and adaptation capabilities for responding to the environmental changes in real time. For this, a structured hierarchical neural network and its learning algorithm are used, and the network is divided into two parts connected with each other via short memory units. This approach is applied to several robots, which learn to interact and participate in a form of the cops-and-robbers game.

3. "Integrating Neural Networks and Knowledge-Based Systems for Intelligent Robotic Control," address the issues involved when integrating these quite distinct systems, which offer very different capabilities. To demonstrate the integration technique and the interaction of the two systems, a two-link robot manipulator is taught how to make tennis like swing. The rule based system first determines how to make a successful swing using rules alone. It then teaches a neural network to perform the task. The rule-based system continues to evaluate the neural network performance, and, if changes in the operating conditions make it necessary, it retrains the neural network.

4. Neural networks and back-propagation are proposed for sensor failure detection in "Use of Neural Networks for Sensor Failure Detection During the Operation of a Control System." The ability to reliably detect failures is essential if a certain degree of autonomy is to be attained. Process control systems are of main interest here. Backpropagation is used for sensor failure detection, and the algorithm is compared via simulations with other fault-detection algorithms.

5. In this article a new neural-network based cross-coupled control algorithm that integrates the cross-coupled control and neural network techniques together is presented. In this neural network based cross-coupled control system, fixed gain PID controller for each individual axis is replaced by a heuristic neural network learning controller the conventional cross-coupled controller is substituted by an efficient neural network cross-coupled controller. Experimental results show that the proposed new neural network based cross-coupled control scheme can be successfully applied to the precise circular tracking problem of a nonlinear uncertain linear motor X-Y table. It is also demonstrated that performance of the neural network based cross-coupled control scheme is superior to the conventional cross-coupled control scheme.

6. An intelligent welding robot system with visual sensors is developed in order to realize full automatic welding of thin mild steel plates including automatic seam tracking and automatic control of welding conditions. A system to detect the shape and dimension of molten pool using CCD camera and a penetration control system using Neural Network in TIG arc welding are investigated. In order to characterize the shape of molten pool, width, length and area of the molten pool were measured, and are used to form the contour of the molten pool as shape parameters. These parameters are input to the neural network, which outputs optimum welding condition to control the penetration of the molten pool. Consequently, if unexpected change occurs in welding conditions, such as root gap, welding speed and so on, the welding system can optimally control the welding condition. The constructed system is tested and found to be effective for penetration control in automatic butt welding of thin mild steel plates.

7. Neural Networks are increasingly finding engineering applications. Most early applications were in the areas of pattern recognition and modeling. This paper shows how neural network models can be used in process control. Two separate techniques are

Illustrated, each with a specific example application. One involves using the network

Itself as the inverse model, by fixing the neural network weights and training on the inputs to give the desired output pattern. The other suggests using the pattern recognition ability of a neural network to identify an appropriate lower order linear model to use for controller design.

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