A method for gyroless missile control in the polar coordinate system is proposed. Motion of a material point under control force is studied. Frequency characteristics of all of the system components have been calculated. A correction filter has been chosen. Stability margin and the transfer coefficient of the con trol system have been determined. Mathematical simulation has been carried out. The missile flight trajecto rys have been obtained.
Abstract. Specific problems arising, when Von Neumann type computer is used as feedback element, are considered. It is shown, that due to specifics of operation this element introduce pure lag into control loop, and lag time depends on complexity of algorithm of control. Method of evaluation of runtime between reading data from sensors of object under control and write out data to actuator based on the theory of semiMarkov process is proposed. Formulae for time characteristics estimation are obtained. Lag time characteristics are used for investigation of stability of linear systems. Digital PID controller is divided onto linear part, which is realized with a soft and pure lag unit, which is realized with both hardware and software. With use notions amplitude and phase margins, condition for stability of system functioning are obtained. Theoretical results are confirm with computer experiment carried out on the third-order system.
The paper discusses the use of artificial neural networks of direct propagation to control objects with nonlinearities such as saturation and a rigid mechanical stop. The multilayer structure of a neural network is considered using the ReLU activation function and the Dropout layer, which allows working with the indicated nonlinearities. The possibility of training a neural network to control piecewise linear objects of a general type is demonstrated. Using the method of generalized inverse neurocontrol, a controller is synthesized that provides an acceptable quality of control in the presence of such nonlinearities. A specific example shows the possibility of creating a direct neuroemulator that repeats the dynamics of nonlinearities such as saturation and a rigid mechanical stop. The work of the neuroregulator in the examples is compared with the PID controller configured in Matlab. All theoretical conclusions are confirmed by numerical modeling of real technical systems with neural network control.
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