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To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown disturbance, a novel discrete-time extended state observer-(DESO) based model-free adaptive sliding mode control strategy with prescribed tracking performance is studied, which only relies on the input/output data of the system rather than explicit model information. Firstly, a compact-form dynamic linearization method is used to reconstruct the discrete-time nonlinear process, where the time-varying parameter linearly connected with the control input is obtained by an adaptive method and the unknown nonlinear term is estimated by a DESO. Then, by considering the prescribed performance and using an unconstrained vector transformed from the constrained tracking error, one model-free sliding mode controller is designed. In addition, a rigorous stability analysis is presented to show the boundedness of the sliding mode function and the prescribed transient-state and steady-state performance of the output tracking error. Finally, the simulations with comparing results verify the effectiveness and superiority of the developed control scheme. K E Y W O R D Sdiscrete-time extended state observer, discrete-time system, model-free adaptive control, prescribed performance, sliding mode control INTRODUCTIONWith the continuous development of engineering technologies, the complexity of the practical system is intensively increasing, which leads to accurate system modeling becoming one of the most difficult tasks. 1 Thus, the control strategies based on mathematical models are not suitable for such complicated systems. To overcome this problem, data-driven control is proposed where only the input and output data are used. Data-driven methods have been applied in many practical scenes, for instance, quadrotor vehicles, 2 automated vehicles, 3 continuum robots, 4 and other industrial process systems. 5,6 Among these data-driven methods, model-free adaptive control (MFAC) approach has aroused a lot of attention because only the input and output data are used without employing explicit or implicit knowledge of the mathematic

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Attitude control of combined service-target system in the post-capture phase has received great attention. A new attitude dynamics of the combined spacecraft with reaction wheels has been established in the author's former work, however, the measurement uncertainty in attitude and angular velocity and uncertainty in the reconfiguration matrix of reaction wheels have not been considered, which may cause huge impact on the system performance. In this paper, a novel combination of disturbance-observer-based dynamic surface control under measurement uncertainty and robust control allocation due to uncertain mass center is investigated for attitude stabilization of the combined spacecraft. Firstly, considering measurement uncertainty, inertia uncertainty, actuator fault and actuator saturation, a new attitude dynamics of combined spacecraft is established. Then, a virtual controller is designed and all the states in the closed-loop system converge to a small neighborhood of zero, where the lumped disturbance is compensated by two stable nonlinear disturbance observers and adverse effect of actuator saturation is addressed by a stable compensator. Finally, in consideration of uncertain location of mass center in the reconfiguration matrix, a LMI-based robust control allocation is employed to deal with the problem of distributing the three axis torques over the reaction wheels. Numerical simulations are presented to illustrate the effectiveness of the proposed method.

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In this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme.

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