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The article gives the possibilities of applications of the backstepping method for solving the problem of synthesis of a control system for the nonlinear dynamic plant with indefinably changing parameters. Has been modified the well-known method of backstepping inclusion in the control system of the state observer, allows for the restoration of unmeasured coordinates with a differentiating unit, due to which substantially simplified the definition of customizable parameters of control device and provides the desired dynamic accuracy of control.

Given in the work the facilities of applying the methods of the synergistic approach for the synthesis of the law of control of nonlinear dynamic plants. To give, robust properties of the control law were proposed by applying a principle of integral adaptation, which permitted compensation for the impact of the external and parametric disturbances. The realization synergistic law of control is carried out by when constructing the analytical design of aggregated controllers, which procure asymptotic sustainability of the control system for nonlinear dynamic plants. For procuring technological invariance proposed principle of the synergistic control. Formulated an extended dynamical model of the system, which included external and internal disturbances. The essence of proposing the approach is to use an extended model of the plant, taking into account disturbances due to the switching on of integrators and procuring compensation for the influence of disturbances, in distinction from known ones, where uses separate models for each disturbance. The suggested methodology has confirmed examples of digital simulation and has shown an efficient proposed approach to tasks synthesis of a nonlinear system of control with dynamical plants, procuring asymptotic sustainability of the control system and recouping unmeasured and internal disturbance.

The paper considered the possibilities of applications of neural network technologies to control a dynamic plant with nonlinear properties. To give the control system the desired dynamic property, the use of a neural network predictive controller is proposed. The model of the control plant is in the form of a multilayer forward-directional neural network, which allows us to construct a controller using generalized equation methods with prediction. A neural network control algorithm with prediction based on minimizing the quadratic quality functional is proposed. The algorithm makes it possible to minimize the root mean square error of regulation and the control signal rate of change. To determine the sequences of optimal control impacts, the application of the Newton-Raphson method is proposed. To reduce computational costs when receiving control signals, the decomposition of the original matrix, represented as a Hess matrix, is carried out. To predict the behavior of a control plant, a formula is proposed for calculating the gradient of a neural network, discrepant by the possibility of its use in the real-time mode of the control. The proposed algorithm of the neural network control with predictive allows higher quality control of complex nonlinear dynamic plants in the real-time mode. [ABSTRACT FROM AUTHOR]