Alexander G. Loukianov

Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

A globally convergent estimator for n-frequencies

In this paper, we propose a solution to the well-known problem of ensuring a simultaneous globally convergent online estimation of the state and the frequencies of a sinusoidal signal composed of n sinusoidal terms. We present an estimator which guarantees global boundedness and convergence of both the state estimation and the frequency estimation for all initial conditions and frequency values.

Robust block second order sliding mode control for a quadrotor

Abstract This paper presents the design of a controller based on the block control technique combined with the super twisting control algorithm for trajectory tracking of a quadrotor helicopter. A first order exact differentiator is used in order to estimate the virtual control inputs, which simplifies the control law design. In addition, the wind parameter resulting from the aerodynamic forces is also estimated in order to ensure robustness against these unmatched perturbations. The stability and finite time convergence of the exact differentiator have been recently proved by means of Lyapunov functions, and therefore the stability analysis of the proposed controller has been carried out along the same lines. The performance and effectiveness of the proposed controller are tested in a simulation study taking into account external disturbances.

Robust Block Decomposition Sliding Mode Control Design

The paper examines the problem of sliding mode manifold design for uncertain nonlinear system with discontinuous control. The original plant first is decomposed such that the problem is divided into a number of simpler sub-problems. Then the block control recursive procedure is presented in which nonlinear sliding manifold is derived. Finally combined high gain and Lyapunov functions techniques are applied to establish hierarchy of the control gains and to estimate the upper bounds of the sliding mode equation solutions.

Discontinuous controller for power systems: sliding-mode block control approach

Based on the complete model of the plant, a sliding-mode stabilizing controller for synchronous generators is designed. The block control approach is used in order to derive a nonlinear sliding surface, on which the mechanical dynamics are linearized. This combined approach enables us to compensate the inherent nonlinearities of the generator and to reject high-level external disturbances. A nonlinear observer is designed for estimation of the rotor fluxes and mechanical torque.

Real-Time Discrete Neural Block Control Using Sliding Modes for Electric Induction Motors

This paper deals with real-time adaptive tracking for discrete-time induction motors in the presence of bounded disturbances. A high-order neural-network structure is used to identify the plant model, and based on this model, a discrete-time control law is derived, which combines discrete-time block-control and sliding-mode techniques. This paper also includes the respective stability analysis for the whole system with a strategy to avoid adaptive weight zero-crossing. The scheme is implemented in real time using a three-phase induction motor.

Discrete-Time High Order Neural Control

The objective of this work is to present recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, that guarantee its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the final chapter presents experimental results related to their application to a electric three phase induction motor, which show the applicability of such designs. The proposed schemes could be employed for different applications beyond the ones presented in this book. The book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. For the first one, a direct design method is considered: the well known backstepping method, under the assumption of complete sate measurement; the second one considers an indirect method, solved with the block control and the sliding mode techniques, under the same assumption. For the third scheme, the backstepping technique is reconsidering including a neural observer, and finally the block control and the sliding mode techniques are used again too, with a neural observer. All the proposed schemes are developed in discrete-time. For both mentioned control methods as well as for the neural observer, the on-line training of the respective neural networks is performed by Kalman Filtering.

Copper and Core Loss Minimization for Induction Motors Using High-Order Sliding-Mode Control

A novel nonlinear affine model for an induction motor with core loss is developed in the well-known (α, β) stationary reference frame, where the core is represented with a resistance in parallel with a magnetization inductance. Then, an optimal rotor flux modulus is calculated such that the power loss due to stator, rotor, and core resistances is minimized, and as a consequence, the motor efficiency is raised; therefore, this flux modulus is forced to be tracked by the induction motor along with a desired rotor velocity by means of a high-order sliding-mode controller, the supertwisting algorithm. Using a novel Lyapunov function, the closed-loop stability of the system is demonstrated. Moreover, a classical sliding-mode observer is designed for the estimation of unmeasurable variables like rotor fluxes and magnetization currents. For the load torque, a Luenberger observer is designed. The performance of the proposed controller is finally studied by simulation and experimental tests. It was observed that the steady-state optimal flux signal corresponds to the load torque profile. This fact suggests that the flux demand is the necessary one to produce the electric torque that can cancel out the load torque.

Discrete time sliding mode control with application to induction motors

This work deals with a sliding mode control scheme for discrete time nonlinear systems. The control law synthesis problem is subdivided into a finite number of subproblems of lower complexity, which can be solved independently. The sliding mode controller is designed to force the system to track a desired reference and to eliminate unwanted disturbances, compensating at the same time matched and unmatched parameter variations. Then, an observer is designed to eliminate the need of the state in the controller implementation. This design technique is illustrated determining a dynamic discrete time controller for induction motors.

Discrete-Time Neural Sliding-Mode Block Control for a DC Motor With Controlled Flux

An adaptive discrete-time tracking controller for a direct current motor with controlled excitation flux is presented. A recurrent neural network is used to identify the plant model; this neural identifier is trained with an extended Kalman filter algorithm. Then, the discrete-time block-control and sliding-mode techniques are used to develop the trajectory tracking. This paper also includes the respective stability analysis for the whole closed-loop system. The effectiveness of the proposed control scheme is verified via real-time implementation.