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This article concerns the output feedback tracking control problem for a class of single‐input‐single‐output (SISO) nonlinear systems in a semistrict feedback form, which possesses both matched and mismatched uncertainties, including parametric uncertainties and unknown nonlinear disturbances. An active disturbance rejection adaptive output feedback control scheme is proposed via backstepping method, in which parametric uncertainties are handled by adaptation law and disturbances are estimated by a series of linear extended state observers (LESOs). The learning burden of LESOs is much alleviated due to active compensation of parametric uncertainties via adaptation. In addition, both matched and mismatched disturbances are estimated in the construction of LESO for each channel of the considered nonlinear plant. Hence, all various of uncertainties can be mostly compensated. Consequently, prescribed transient performance and steady‐state tracking accuracy can be guaranteed. Theoretical analysis reveals the tracking error can be made arbitrarily small. Furthermore, exponentially asymptotic output tracking can be achieved when unknown disturbances are constants. Finally, comparative simulation studies are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Uncertainties, especially mismatched uncertainties, pose great challenges to high accuracy tracking controller design for mechanical servosystems. In this article, a novel recursive robust integral of the sign of the error (RISE) control method is proposed for mechanical servosystems with mismatched uncertainties. In the controller development, two auxiliary error signals are introduced into the recursive backstepping design framework, and then, RISE feedbacks are synthesized to eliminate the matched and mismatcheduncertainties simultaneously. Moreover, to reduce the design conservatism, an adaptive recursive RISE control law is also developed for mechanical servosystems suffering from both parametric uncertainties and unmodeled disturbances, in which desired-trajectory-based adaptation law is synthesized to achieve compensation for parametric uncertainties. The proposed control methods can theoretically achieve remarkable asymptotic tracking performance with zero steady-state error in spite of matched and mismatched time-variant uncertainties. The proposed controllers are applied to an actual hydraulic servosystem and comparative experiments are performed to verify their effectiveness. [ABSTRACT FROM AUTHOR]

• The proposed controller accounts for both matched and mismatched modeling uncertainties. • Both largely unknown matched and mismatched modeling uncertainties can be compensated. • The presented controller can handle parametric uncertainties with only position measurement. • The proposed controller has better adaptability to severe working conditions. In this paper, a nonlinear adaptive output feedback robust controller is proposed for motion control of hydraulic servo systems in the presence of largely unknown matched and mismatched modeling uncertainties. Different from the existing control technologies, the presented hydraulic closed-loop controller which can deal with strong matched and mismatched parametric uncertainties is synthesized via the backstepping technique. Specially, a nonlinear disturbance observer which can estimate the largely mismatched disturbance is integrated into the design of the linear extended state observer to obtain estimation of unmeasurable system states, uncertain parameters and strong disturbances simultaneously. In addition, the projection-type adaptive law is synthesized into the design of the resulting controller. More importantly, the global stability of the whole closed-loop system is strictly guaranteed by the Lyapunov analysis. Furthermore, the effectiveness and practicability of the presented control strategy have been demonstrated by comparative experiments under different working conditions. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an advanced nonlinear controller for hydraulic system to achieve asymptotic tracking with various disturbances. To accomplish this study, a multilayer neural-networks (NNs) estimator is first developed to improve the compensation accuracy of model-based feedforward control terms, which can greatly reduce the uncompensated disturbance, then a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the multilayer NNs estimator to deal with the residual mismatched disturbance, in which the RISE feedback gain is adapted online to further decrease the high-gain feedback. At last, by considering the inwardness of matched disturbance in hydraulic systems, it is estimated by another multilayer NNs fully with the residual functional reconstruction inaccuracies handled by a novel adaptive term. As a result, theoretical analysis reveals that the proposed controller guarantees a semiglobal asymptotic stability. Extensively comparative experimental results verify the priority of the proposed control strategy, and a 0.2% dynamic tracking accuracy is achieved. [ABSTRACT FROM AUTHOR]

Suspension operation reliability is one of the most significant performance indexes that concerns maneuvering stability and drive safety. In this paper, in order to guarantee good suspension reliability, an adaptive fault detection and isolation scheme is proposed for quarter-car active suspension systems with parametric and nonlinear uncertainties. To realize fault diagnosis for active suspensions, an adaptive fault detection estimator and several fault isolation estimators are designed to generate state residuals. Corresponding adaptive thresholds are developed to help judge the occurrence and type of possible faults. In the process of constructing state estimators, the uncertain parameter is updated online so that good sensitivity of fault detection can be achieved. Illustrative simulation is carried out to validate the effectiveness of the fault diagnosis scheme, and results indicate that the proposed method is sensible to sudden faults and maintain robustness to model uncertainties appearing in suspension systems. [ABSTRACT FROM AUTHOR]

This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

A desired compensation adaptive controller is proposed in this paper for precision motion control of electro-hydraulic servo systems, with consideration of the nonlinearity, modeling uncertainty, and especially the severe measurement noise arising from actual state feedbacks and deteriorating the control performance significantly. To alleviate the noise, actual states in the model-based compensation design are replaced with corresponding desired values. Considering that the general hydraulic system model contains unmatched modeling uncertainties (e.g., unmodeled nonlinear friction), an innovative approach to construct the desired values of the intermediate state variables is proposed in backstepping design procedure. It is then applied especially to the load pressure state, which appears in the system in a nonlinear way, and the discontinuous sign function is approximated by a continuous function to facilitate the controller design. As a result, the adaptive compensation and the regressor in the proposed controller depend on the desired trajectory and online parameter estimates only. Hence, the effect of measurement noise can be reduced and then high control performance is expected. Theoretical analysis reveals that the proposed controller can guarantee a prescribed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. Moreover, it can guarantee the asymptotic tracking performance when subjected to parametric uncertainties only. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

Parametric uncertainty associated with unmodeled disturbance always exist in physical hydraulic systems, and complicate the advanced nonlinear controller design. In this paper, an adaptive compensation with a robust integral of the sign of the error (RISE) feedback is developed for high precise tracking control of hydraulic motion system. To handle both payload and hydraulic unknown parameters in one controller, a chain of integrator nonlinear system model is first derived, and an adaptive RISE controller is then proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. The major feature of the proposed controller is that it can theoretically guarantee asymptotic tracking performance with a continuous control input, in the presence of various parametric uncertainties and unmodeled disturbances such as unconsidered dynamics as well as external disturbances via Lyapunov analysis. However, the proposed controller takes the acceleration as a system state, which usually suffers heavy noise pollution and thus cannot be utilized directly in actual control. To solve this practical issue, in this paper, a tracking differentiator is employed to extract high-quality acceleration signal and to make the proposed controller feasible execution. The effectiveness of the proposed nonlinear controller is demonstrated via comparative experimental results. [ABSTRACT FROM PUBLISHER]

When performing periodic tasks, the modeling uncertainties will also present some periodicity. In this paper, by appropriately applying Fourier series approximation, a practical nonlinear adaptive repetitive controller is proposed for motion control of hydraulic servomechanisms to learn and compensate the periodic modeling uncertainties. Robust control term is also constructed to effectively attenuate the effect of approximation errors, and thus asymptotic tracking performance is achieved. In addition, robustness is also discussed with respect to other nonperiodic disturbances, which reveals a guaranteed transient performance and steady-state tracking accuracy can be achieved by the proposed controller with a practical assumption. Compared to the traditional repetitive controllers, the major advantage of this controller is that it not only requires little exact knowledge of the system dynamic structure or its parameters, but also greatly reduces the noise sensitivity and heavy memory requirements. Comparative experimental results are obtained to verify the high accuracy tracking performance of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation. Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms. However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model. Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction. In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation. In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller. The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis. The effectiveness of the proposed controller is demonstrated via comparative experimental results. [ABSTRACT FROM PUBLISHER]

Continuous friction compensation along with other modeling uncertainties is concerned in this paper, to result in a continuous control input, which is more suitable for controller implementation. To accomplish this control task, a practical method, named as robust integral of the sign of the error controller, is synthesized with a continuous differentiable friction model for high-accuracy motion control of a dc motor. To reduce the noise sensitivity and further improve the tracking accuracy, a desired compensation technique is employed in the proposed controller, in which the model compensation term depends on the reference trajectory only, and its global stability is guaranteed by a proper robust feedback law. Furthermore, the proposed controller theoretically guarantees an asymptotic output tracking performance even in the presence of modeling uncertainties, which is very important for high-accuracy control of motion systems. Comparative experimental results are obtained for the motion control of a dc motor drive system to verify the high-performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

In this paper, an output feedback nonlinear control is proposed for a hydraulic system with mismatched modeling uncertainties in which an extended state observer (ESO) and a nonlinear robust controller are synthesized via the backstepping method. The ESO is designed to estimate not only the unmeasured system states but also the modeling uncertainties. The nonlinear robust controller is designed to stabilize the closed-loop system. The proposed controller accounts for not only the nonlinearities (e.g., nonlinear flow features of servovalve), but also the modeling uncertainties (e.g., parameter derivations and unmodeled dynamics). Furthermore, the controller theoretically guarantees a prescribed tracking transient performance and final tracking accuracy, while achieving asymptotic tracking performance in the absence of time-varying uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

Structured and unstructured uncertainties always exist in physical servo systems and degrade their tracking accuracy. In this paper, a practical method named adaptive robust control with extended state observer (ESO) is synthesized for high-accuracy motion control of a dc motor. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties) but also the unstructured uncertainties (i.e., nonlinear friction, external disturbances, and/or unmodeled dynamics). Adaptive control for the structured uncertainty and ESO for the unstructured uncertainty are designed for compensating them respectively and integrated together via a feedforward cancellation technique. The global robustness of the controller is guaranteed by a feedback robust law. Furthermore, the controller theoretically guarantees a prescribed tracking performance in the presence of various uncertainties, which is very important for high-accuracy control of motion systems. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

• A new asymptotic output feedback controller with NN-based compensation is developed. • An adaptive-gain law is synthesized for the state observer gain. • One NN with low computation is integrated for unknown dynamics compensation. • Experimental results of a motor system reveal the validity of the presented method. Measurement noise and unknown dynamics including friction, parametric uncertainties, external disturbances and unmodeled dynamics, broadly exist in any electromechanical system and worsen their control performance. To address this issue, this paper develops an output feedback control scheme with neural network (NN) based unknown dynamics compensation for DC motor systems. First, to avert using the noise-polluted velocity signal, a state observer with an online adapted gain is adopted, which attenuates the impact of measurement noise on tracking accuracy and reduces the conservatism of observer gain selection. Second, one NN with low computation and ease to design, is employed for unknown dynamics compensation, which is beneficial for practical applications. Then a composite control law is constructed to achieve high-accuracy tracking performance. The stability analysis reveals the tracking error can asymptotically converge to zero while facing time-variant unknown dynamics. Results of comparative experiments validate the superiority of the presented method. [ABSTRACT FROM AUTHOR]