Papers by Keyword: Robust Control

Abstract: This study investigates the deployment of adaptive neural network-based control strategies for nonlinear dynamic systems, emphasizing the integration of Echo State Networks (ESNs) into a feedforward-feedback control architecture. Traditional controllers relying on precise mathematical modeling often fail to cope with the complexity of systems exhibiting high nonlinearity, time-varying parameters, and external disturbances. The proposed ESN-based approach harnesses reservoir computing to construct a lightweight, data-driven model capable of accurately capturing system dynamics in real time. The feedforward module provides anticipatory control actions, while the feedback loop compensates for deviations, enabling rapid convergence and robustness against parametric drift. Comparative analysis with conventional PID and LQR controllers reveals superior performance in terms of tracking accuracy, stability, and noise resilience. Preliminary simulations predict reduced steady-state error and improved dynamic response even under uncertain operating conditions. This architecture presents a scalable and efficient alternative for advanced applications in robotics, aerospace, and industrial process control. The findings affirm the viability of ESNs in redefining adaptive control paradigms by combining interpretability, computational efficiency, and real-world adaptability. Reference to this paper should be made as follows:MCE 2025, MCE825. (2025) ‘Adaptive neural network-based feedforward-feedback controller for nonlinear dynamic systems.

Abstract: The paper discusses the use of differential filters in control algorithms. The filters are designed to determine the derivatives of the input signal and eliminate measuring and quantization noise. The differential filters improved the quality of control, with the results being better than those obtained with the classic Finite Difference Method (FDM). The primary purpose of the study was to employ the differential filters in a real-time control algorithm, which requires appropriate derivatives. The control process involved applying a method of aggregation of state variables, based on signal derivatives, which can be used for non-linear dynamic systems. The experiments were conducted on a test stand with a pneumatic muscle acting as the plant to be controlled.

Abstract: A closed-loop control algorithm is used for stabilization of a vibrating nonlinearstrain gradient micro Euler-Bernoulli cantilever beam usinglinear piezoelectric actuation.In this paper, the governing partial differential equation (PDE) of the nonlinear strain gradient beam with piezoelectric actuator is obtained. Galerkin projection method is utilized to reduce the system’s PDE equation of motion into a set of nonlinear ordinary differential equation (ODE) model. The nonlinear system is controlled by a robust linear controller which ensures the stability of the nonlinear system. Numerical simulations are investigated to demonstrate the effectiveness and performance of the designed control scheme.

Abstract: This paper presents a method for the estimation of unknown disturbances for high precision gimbal systems. Alternative to the classical methods of model inversion and filtering, we employ an asymptotically stabilizing controller which achieves the estimation process even in the presence of unstable zeros. The architecture provides an input equivalent disturbance which can be thought to capture all real disturbances in the system as well as virtual ones such as unmodelled dynamics and nonlinearities. The proposed method is illustrated on a 2-axis gimbal system where system identification is followed by the design on an integral linear quadratic regulator as the stabilizing controller which forms the base of the disturbance observer. It is seen that a random unknown disturbance is estimated successfully in the presence of additional gyro noise.

Abstract: In this paper, the basic principle and design procedure of the quantitative feedback theory (QFT) are summarized, then control system of a UAV is designed with the QFT theory. The QFT theory can solve the robust design problem of the UAV control system due to the model parameters uncertainty perfectly. And a method of robust design for UAV is put forward from engineering application perspective. Compared with traditional control method, the simulation result shows that the control system designed by the QFT theory not only has more perfect control effect but has significant value in the engineering application.

Abstract: This paper addresses issues related to robust control for an airbreathing hypersonic flight vehicle. Owing to aero-propulsion couplings caused by the unique structure shape, the model of the vehicle is greatly nonlinear and complex, which presents an enormous technical challenge for control. The nonlinear model is transformed into a linear fractional transformation (LFT) model, and a robust gain-scheduling controller based on linear parameter-varying control (LPV) with full block multipliers is obtained. Simulations illustrate great improvements of the dynamic performance in closed-loop system.

Abstract: This paper is devoted to robust control design for block multilinked nonlinear dynamical systems. Transformation of the block system to the single block system is proposed. For the considered block systems function of Lyapunov is designed. It is proved if the number of controls is equal to or more than the number of state variables of the block, then in the given area the closed-loop system conditions of stability followed controllability conditions. Control design accounts limitations of controls and state variables. Modeling results for nonlinear objects control systems are presented.

Abstract: In this paper, the state space model of neutral point clamped three-level grid-connected inverter is detailed derived and an Robust Predictive Control (RPC) method is proposed. In the proposed method, the total 27 switching vectors composed of a finite set from which the optimal switching vector is selected according to a cost function. The robust performance is improved just by increasing switching frequency. Experimental results show that the proposed method has a flexible control purposes and we can optimize the synthesize performance just by adjusting weighting coefficients of cost function. In addition, RPC controller has a robust performance in tracking output reference current and balancing neutral point voltage.

Abstract: A novel robust control scheme for decentralized generator excitation and valve coordinated control systems to improve power system stability is proposed. By utilizing generator terminal voltage magnitude and phase angle to represent the interactions among generators, decentralized generator excitation and valve coordinated control in multi-machine power systems is achieved. The control is realized by robust parametric approach. Simulation results show that the proposed robust parametric coordinated control can improve power system stability.

Abstract: Robust stabilization of a class of uncertain fuzzy systems is considered in this paper. The fuzzy systems not only exit disturbances which do not satisfy the so-called matching condition and contain uncertainties in the input channel, but have uncertainties in the state matrix. By using Lyapunov function, the robust state-feedback controllers are constructed. The controllers guarantee that the states of systems are robust stabilization. A numerical example is given to show the effectiveness of our method.