Neural network method of adaptive control system of imitation of physical forces
This research examines the subject area of physical forces simulation systems, implemented on the basis of controlled running platforms. The time spent by the control system to receive and process information about the state of the user and the system causes a software and hardware delay that prevents the system from responding in a timely manner to the user's natural movement. The control system delay problem cannot be solved using direct data of the states of the man-machine system. The aim of the presented research is to develop a new control method that allows analyzing the state of the user and the platform, forecasting his movements and organizing the process of managing the system for simulating physical forces. The method is implemented using neural networks. The scientific novelty of the method includes in the use of neural networks to solve the problems of forecasting user actions and automating decision-making to control the system for simulating physical forces. Each presented neural network is formed to perform separate tasks. The first is to create a forecast of changes in the states of a man-machine system. The second is to determine whether the forecasted state belongs to any state in the historical data. The third determines the required change in the states of the parameters of the man-machine system to achieve the forecasted state. The possibilities of using the described approach are presented on the example of a treadmill that adapts to the real parameters of the user's locomotion. The results obtained confirm a significant reduction in the complexity of the implementation of the control process after applying the neural network method. The area of application of the neural network control method is adaptive information systems and automatic control systems, in which it is necessary to minimize the system delay time and response to user locomotion.