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This article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller. [ABSTRACT FROM AUTHOR]

This paper concerns high‐accuracy tracking control and parameter estimation for semi‐strict feedback systems with unknown time‐varying parameters. The unsmooth and discontinuous parameters and matrix parameters are all taken into consideration. An optimized indirect adaptive controller based on Z‐swapping and improved least‐square algorithm is developed to handle parametric and unmolded uncertainties simultaneously. Each design index of this adaptive law can be adjusted freely according to the order of the magnitude of unknown parameters and state variables so that the coupling of the estimating process can be avoided. The Lyapunov‐based stability analysis illustrates that the proposed method can obtain a prescribed estimating result as well as transient and final tracking performance. Simulations based on two common systems verify the outstanding performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on high-accuracy motion control for electromechanical servo systems with time-varying parameter estimation. A robust indirect adaptive controller based on a nonlinear X-swapping technique is proposed to achieve the online estimation of the time-varying parameters. The time-varying parametric uncertainties can be well compensated via the adaptive feedforward cancellation technique, and the residual model uncertainties are suppressed by a nonlinear robust control law. The Lyapunov-based stability analysis illustrates that the proposed method obtains a prescribed estimating result and an excellent asymptotic tracking performance. Extensive experimental results are acquired to affirm the applicability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article proposes an asymptotic adaptive command filtered control approach for uncertain nonlinear systems with parametric uncertainties, mismatched and matched disturbances. To accomplish the task, a disturbance observer (DO) only with one tuning parameter is firstly used to attain the disturbance compensation. The parameter uncertainties can be addressed via composite updated laws. Then, by judiciously combining DO, adaptive control and command filter technique, a novel command filtered controller with adaptive-gain auxiliary systems is developed to attain asymptotic tracking and shun "explosion of complexity". The system stability is proved by utilizing the Lyapunov function. Extensive experimental results uncover the preponderance of the exhibited strategy. • A novel command filter-based adaptive controller is proposed for uncertain systems with mismatched disturbances. • The controller with adaptive-gain auxiliary systems is developed to attain the asymptotic tracking performance. • The presented disturbance observer only with one tuning parameter does not need any prior assumption on disturbances. [ABSTRACT FROM AUTHOR]

This article focuses on the asymptotic tracking control problem for uncertain nonlinear systems subject to both multiple disturbances and parametric uncertainties. To address this issue, a parameter adaptation law is synthesized to deal with the parametric uncertainties, and an adaptive-gain disturbance estimator (ADE) is constructed to estimate the mismatched and matched disturbances, and compensate them in feedforward channels, which eliminates the impact of disturbances on tracking performance. Meanwhile, an updated law for estimator gain driven by the estimation errors is utilized in the ADE when facing unknown upper bounds of disturbances, which reduces the conservatism of estimator gain selection and is beneficial to practical implementation. Based on the parameter adaption technique and the presented ADE approach, a composite controller is proposed to ensure an excellent asymptotic output tracking performance. The stability analysis shows the proposed controller can attain asymptotic tracking performance in the presence of both time-variant disturbances and parametric uncertainties. Comparative simulation results of the application to a robot manipulator reveal the validity of the developed approach. [ABSTRACT FROM AUTHOR]