Robot systems, due to their unique flexibility and economy, are widely used in modern industry and intelligent manufacturing. The parameters of the system are unknown, and traditional parameter estimation methods are difficult to achieve fixed time convergence, which leads to extremely position tracking control problem. In addition, the transient and steady-state performance of the robot system is difficult to specify in advance. In this article, a novel composite learning fixed-time (FxT) control strategy is proposed for the robotic systems to deal with these issues. The funnel control (FC) is utilized to transform the original error system into a new error dynamics with transient performance constraints. The two-phase nonsingular FxT sliding mode surface is constructed to avoid the singularity problem. Then, the filter operation is introduced to obtain the expression of parameter estimation error and is used to design the composite learning law. To achieve parameter estimation, a FxT composite learning law based on online historical data and regression extension is proposed, where the interval excitation (IE) is considered in the adaptive law. Finally, the designed adaption is incorporated into the nonsingular FxT sliding mode control to achieve tracking control. Moreover, the comparison of three different controllers is made to demonstrate the benefits of the developed control strategy.

This brief presents a novel one-step adaptive parameter estimation framework for identification of unknown asymmetric dead-zone characteristic parameters (e.g., width and slopes) in the sandwich systems, which avoids using the intermediate variables or carrying out a two-step recursive identification procedure. By applying a continuous piecewise linear neural network (CPLNN), the asymmetric dead-zone nonlinearities can be represented into a parameterized form, where the dead-zone characteristic parameters can be derived based on the online updated CPLNN weights. Moreover, after representing the sandwich system into an augmented system, an adaptive observer is designed to reconstruct the immeasurable internal variables, and an adaptive law with guaranteed convergence is used to online update the CPLNN weights so as to estimate the dead-zone parameters. In this adaptive law, the parameter estimation error information is extracted using simple filters and introducing several auxiliary variables. It is proven that the observation error and estimation error both converge to a small set around zero. The efficiency of the estimation method is exemplified via practical experiments on an $X$ - $Y$ positioning platform.

The Hammerstein model has proven its ability for modeling many industrial servo systems, but the corresponding parameter estimation remains as a challenging problem, especially for the purpose of achieving fast convergence. This article presents a fixed-time (FxT) adaptive parameter estimation scheme for Hammerstein systems with an asymmetric dead-zone to ensure the convergence of estimated parameters in fixed-time. A continuous piecewise linear function neural network is adopted to model the nonlinear dead-zone dynamics to remedy the use of intermediate variable. To avoid using the system state information, a K-filter is used to construct a relationship between the unknown system parameters and the input/output signals. Then, a new adaptive law based on the parameter error and the FxT scheme is developed to online estimate the lumped unknown parameters and ensure the fixed-time convergence, where an online updated auxiliary variable of regressor is suggested to eliminate the impact of the regressor on the convergence performance. Both numerical simulations and experimental results are given to demonstrate the effectiveness of proposed methods.

The Hammerstein model has been successfully used to model various industrial systems, while the parameter estimation of such a model is difficult. In this article, a novel adaptive parameter estimation scheme is proposed for the continuous-time Hammerstein model with an asymmetric dead-zone, which avoids using the immeasurable intermediate variables and system states. First, a continuous piecewise linear neural network is adopted to reformulate the dead-zone dynamics, thus facilitating the derivation of dead-zone characteristic parameters. By applying the K-filter operation, an integrated parametric model of the Hammerstein system with input/output measurements is obtained, which allows separating the observer from the parameter estimation. Hence, two adaptive laws based on the estimation error are given to obtain the estimation of unknown parameters of the dead-zone and the linear subsystem. Then, an observer with the estimated parameters is designed to reconstruct the unknown system states. Theoretical analysis demonstrates that both the observer and estimation errors converge to zero. The validity of the proposed methods is verified by numerical simulations and practical experiments on an articulated manipulator.

This brief presents a novel control method for half-vehicle active suspension systems driven by hydraulic actuators. First, a coordinate transform approach is introduced to reformulate the strict-feedback system into a canonical form, by which the widely used backstepping method is not required. Then, a modified high-gain observer (HGO) is studied to rebuild the unknown system states of the nonlinear active suspension system. To eliminate the effect of unknown nonlinearities, a simple robust unknown system dynamics estimator (USDE) with only one tuning parameter is developed in terms of the system input and output only. Finally, the observer and estimator are integrated to design an output feedback controller to regulate the vehicle motion. Comparative experiments are carried out to illustrate the effectiveness of the proposed method.

The cable-driven exoskeleton system can provide long-distance power transmission for limb movement, simplify exoskeleton design and thus enhance the portability, flexibility, and wearable comfort. However, the cable-driven exoskeleton system contains many nonlinear dynamics because of the cable’s elasticity and friction, which poses a great challenge for its accurate modeling and control. This article establishes an integrated model for double-cable-driven exoskeleton systems, which considers comprehensive friction dynamics including Coulomb, Stribeck, viscous, and asymmetric dynamics. To address the difficulties in the online identification induced by the cable-conduit configuration, a continuous piecewise linear neural network is used to reconstruct the friction dynamics and thus avoid the use of immeasurable intermediate variables. This allows the integrated model to be used for both mechanism analysis and online identification. To achieve parameter identification, a low-pass filter operation is applied to obtain the estimation error, and then a fixed-time adaptive parameter estimation scheme based on the estimation error is developed. Finally, experimental results demonstrate the validity of the developed model and the efficiency of the proposed parameter identification method.