This paper addresses the problem of a synthesis of model predictive control for quadrupedal robots with adaptive change of weight matrices using a neural network. The robot model in the predictive control algorithm is considered as a single rigid body, which is affected by forces in the contact spots. The control method is a combination of a swing leg and ground force controllers with the use of whole-body impulse control for the latter. A fully connected neural network is applied for automatic tuning of weight coefficients used in convex model predictive control. The effectiveness of the proposed approach is demonstrated using numerical simulations, which provide a significant reduction in errors for various robot speeds.

This paper is addressed to a problem of synthesis of hyperexponential control algorithm based on Implicit Lyapunov Function (ILF) method using model-free technique for synchronous motors. The nonlinear motor model under consideration corresponds to a non-salient synchronous motor with surface–mounted permanent magnets installed in the rotor. The model-free approach is applied to replace the complex nonlinear motor model with an ultra-local model with simple dynamics. Then hyperexponential control law is formulated using the theorem with linear matrix inequalities. The effectiveness of the approach is demonstrated via realistic numerical simulations with delays in control channel and noised measurements.

This paper is addressed to the problem of state estimation for permanent magnet synchronous motors (PMSMs) with uncertain parameters. The proposed observer of flux, rotor position and speed uses measurements of stator currents and control voltages only. Moreover, it is assumed that all the motor parameters are unknown. The designed estimation algorithm generates estimates of several parameters that are used by the state observer. Presented approach is based on motor model transformation that results in a linear regression model with unknown parameters and application of Kreisselmeier’s dynamic extension of the regressor with suitable mixing. It is shown that parameter and state estimates converge in a fixed time under some reasonable assumptions. Simulation results demonstrate efficiency of the proposed solution for a typical scenario of motor operation.

This study focuses on detecting and isolation of faults in the inertial navigation system (INS) of an unmanned surface vessel (USV) which parameters are unknown. The INS comprises units for measuring angular and linear velocity. The proposed method employs full-order Luenberger observers to construct directional generators of residual signals. To determine the unknown parameters of the USV, Kreisselmeier’s dynamic extension of the regressor is applied that assists in maintaining the excitation level of the original regressor. The latter is supplied with an ad-hoc modification resulting in a fixed time convergence of estimation errors to zero. Through simulations, the study demonstrates the effectiveness and reliability of the algorithm in accurately identifying faults in each sensor and the improvement of transients of parameter identification compared to DREM-based estimator.

This paper addresses to the problem of a non-linear MIMO systems control. A class of non-linear parameter uncertain systems operating under unknown bounded disturbances is considered. It is assumed, that mathematical model of such system can be decomposed on linear and non-linear dynamics. Proposed control algorithm is based on the method of consecutive compensator. The only required parameter to be known for the controller synthesis is a relative degree of linear part of plant. The effectiveness of the control method is demonstrated experimentally using the laboratory platform named «Twin Rotor MIMO System». The proposed method is compared with standard PID controller. Experimental results show that the transient behaviour of the developed control algorithm provides higher accuracy and performance, especially for the case of model parameters deviation from their nominal values.

The article describes an active learning method in studying the course "Integrated Systems Design and Control" for the masters program "System Analysis and Control". The method is based on involving of students to acquisition of a new knowledge in learning process, finding optimal solutions supported by practical activities. Strengthening of theoretical knowledge is supposed to be performed through workshops and discussions of the learnt materials. Practical learning is performed within the laboratory practice on a specially designed bench that described in this article.