Simple Observer Based Synchronization of Lorenz System with Parametric Uncertainty

Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

Novel adaptive feedback synchronization scheme for a class of chaotic systems with and without parametric uncertainty

In the paper, based on a combination of the robust control approach and an adaptive control technique, a design procedure of a new robust adaptive controller is proposed for power system load-frequency control with system parametric uncertainties. The system parametric uncertainties are obtained by changing parameters by 30% to 50% simultaneously from their typical values. In the design of the proposed controller, only the bounds of system parameters are required. The motivation of combining the robust control with an adaptive control is to use the robust control approach to deal with the small parametric uncertainties and to use the adaptive control for large parametric uncertainties so that stability of the overall system and good performance can both be achieved for all admissible uncertainties. The simulation results demonstrate that for the example system the proposed load-frequency controller can achieve good performance even in the presence of the generation rate constraint (GRC).

Uncertainty quantification in a resonant nonlinear MEMS structure

In this paper an adaptive sliding controller is presented for second order nonlinear systems with system parameter uncertainties. Using an adaptive technique to estimate uncertain system parameters, an adaptive sliding controller is then proposed to ensure that the sliding condition is maintained in finite time. The proposed adaptive sliding control scheme can be implemented without the requirement that the bounds of uncertain system parameters should be known in advance. Furthermore, the control chattering drawback occurred in the classical sliding mode control can be alleviated in the proposed control scheme. To show the effectiveness of the proposed scheme, some simulation results are presented.

This paper presents a general methodology for computer-aided multi-criteria optimal design which allows all the different design criteria to be traded off while accounting for modeling and loading uncertainties. Uncertain system parameters are modeled as random variables with a prescribed joint probability density function, whereas the loads are modeled as stochastic processes. Second-order probabilistic descriptors are combined with approximate extreme response theories to obtain reliability estimates for the structural systems. A multi-criteria optimal design methodology, based on a preference aggregation rule, is used in this formulation. Optimization is carried out by generating and solving a sequence of explicit approximate problems. The uncertainty in system parameters is taken explicitly in the analysis and its effect is investigated on the optimal design. Uncertainties are important because they can change the optimal design significantly. A numerical example demonstrates the performance and efficiency of the proposed methodology.

Adaptive Backstepping Control for A Twin Rotor MIMO System

A method is presented to efficiently approximate the failure probability of structures subjected to time-variant loads, where the system of loads and structure may have uncertain parameters. The method uses response surface methodology in conjunction with the fast integration technique suggested by Wen and Chen, to provide a limit-state formulation that is computationally simple to solve based on a small number of response time histories. The system reliability may then be quickly computed by first-order reliability method (FORM)/ second-order reliability method (SORM) or Monte Carlo simulation. Sensitivity analysis is performed to determine the effect on the failure probability of changes to the system parameters, which can be important when determining whether uncertainty in a given system parameter is significant. An empirical measure of the accuracy of the response surface approximation is presented.

Aiming at the tip trajectory tracking against system parameter uncertainty and disturbance during the painting process of the spray painting manipulator, a sliding model active disturbance rejection trajectory tracking controller is designed integrating terminal sliding mode control (SMC) approach and active disturbance rejection control (ADRC) technique. The reference curve trajectory of the tip position/velocity is planned by the kinematics equation. in which the compensation is taken into account for the deflection of the manipulator. The extended state observer (ESO) is used to estimate the total disturbance consisting of the uncertain system parameters and the uncertain interference resulting from deflection and load. Utilizing non-singular terminal sliding mode control, a feedback control law is designed to achieve fast and accurate trajectory tracking. Simultaneously, according to the actual system parameters in the engineering, the simulation validation is given to show that the proposed control strategy can guarantee good tracking performance of the closed-loop system.

High-fidelity quantum gates are crucial for achieving fault-tolerant quantum computing; however, decoherence significantly reduces gate fidelities during long operation times. Although optimal control techniques can theoretically minimize these operation times, they often neglect realistic uncertainties in system parameters. In this work, we demonstrate that by using robust optimal control strategies, the cross-resonance gate in superconducting systems can be operated within 64 ns, achieving fidelities of [Formula: see text] while maintaining robustness against up to 10% uncertainty in a single parameter. Alternatively, by extending the control time to 71 ns, we achieve fidelities of [Formula: see text] with robustness against up to 3% uncertainty. Our results identify the minimal control times attainable with experimentally feasible pulses and system parameters, as well as the maximum allowable static parameter error for high-fidelity operations. Furthermore, we demonstrate simultaneous robustness against both static and time-dependent errors by generating 100 ns control pulses ([Formula: see text]) that maintain robustness against 10% static parameter error and time-dependent parameter fluctuations two orders of magnitude stronger than typical experimental noise. These findings demonstrate a viable open-loop strategy for implementing fast, high-fidelity quantum gates in the presence of realistic system uncertainties that would otherwise degrade conventional control pulses.

To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{1}}{\ ext{0.8590}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{2}}{\ ext{0.0831}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} in the x and y directions, respectively.

Fractional order adaptive fuzzy continuous non-singular terminal sliding mode control is proposed for electrical aerodynamics load simulator system which is subjected to extra torque disturbance and parametric uncertainties. A fractional order sliding surface is constructed and a continuous non-singular terminal sliding mode control law is derived to ensure faster convergence and precise control without singularity problem. Fractional order adaptive fuzzy logic system is used to estimate the unknown extra torque disturbance. Moreover, nonlinear friction is compensated using LuGre model. Practically, electrical aerodynamics load simulator system is affected by additional un-modeled dynamics due to uncertainty in system parameters and friction model combined with fuzzy approximation error. The un-modeled dynamics are compensated using fractional order adaptive law which is derived using Lyapunov function. The validity and effectiveness of proposed control scheme are verified using numerical simulations.

Adaptive robust H∞ control for double support balance systems

A passivity-based adaptive control law is presented, which transforms the permanent magnet synchronous motor (PMSM) with v/f controller into an equivalent passive system. It is proved that the equivalent system can be asymptotically stabilized at different equilibrium points without any influence of undeterministic parameters. Simulation results show the proposed control law is very effective and robust against the uncertainty in system parameters.

In this paper, the real time tracking performance of the fuzzy PID controller has been evaluated on nonlinear 3-DOF helicopter system. Quanser 3-DOF helicopter system has been utilized as experimental setup. Three separate fuzzy PID controller have been employed so as to control three state of the system. The dynamics of the system has been altered via counterweight in order to examine the performances of the both controller for parametric uncertainty in system parameter. The results reveal that the fuzzy PID controller leads to a good tracking performance and has better parametric uncertainty rejection capability than LQR.