전기-유압서보시스템을 위한 비선형 관측기 기반의 강인한 백스테핑 제어

Abstract

In this dissertation, we present robust backstepping position control methods, based on the Lyapunov method, using nonlinear observers for electro-hydraulic servo systems (EHSs) in the presence of disturbances. We focus on designing the controllers to solve issues for the EHS such as output feedback, load force and friction disturbance, and tolerance for tracking performance. First, we present a position tracking control based on a passivity-based output feedback nonlinear control. A high gain observer is designed to estimate the full state, and the high gain technique is used to reduce the effects of nonlinear terms. The passivity-based controller is implemented for position tracking. Although the passivity-based control is simpler and more straightforward than the backstepping algorithm, the passivity-based controller guarantees only asymptotic stability of the tracking error dynamics. Therefore, it is difficult for the passivity-based controller to be designed with a high gain observer since the exponential stabilities of the quasi-steady-state model and the boundary layer system are required for the stability of the singular perturbed system. This dissertation presents a solution to this problem, where the controller gain is selected such that the origin of the tracking error dynamics is exponentially stable. We prove that the closed-loop system is the asymptotically stable under the selection criteria using the singular perturbation method. Therefore, the observer gain is selected considering of the controller gain. Second, the design and implementation of a nonlinear position tracking controller with a disturbance observer (DOB) for EHSs in the presence of a biased sinusoidal disturbance are studied. The proposed method is designed for implementation on a rotational joint driven by a linear EHS. In this type of EHS, the main disturbance consists of constant friction and sinusoidal load torque and is thus a biased sinusoidal signal with unknown frequency. The disturbance observer, whose simplicity results from it requiring only mechanical parameters, is designed as a second-order high pass filter for estimating the disturbance without any additional algorithms. The nonlinear controller is designed to track the desired position, operating as a near input-output linearizing inner-loop load pressure controller and a backstepping outer-loop position controller. Variable structure control is implemented in order to compensate for the error in the disturbance estimation. The desired load pressure is generated using the differential flatness property of the EHS mechanical subsystem. The disturbance includes not only a biased sinusoidal signal, but also the effects of the modeling assumptions and the parameter uncertainties within the bandwidth of the DOB; it can be canceled out using the proposed method. Finally, we propose a high gain disturbance observer (HGDOB) based robust backstepping controller with a position tracking error constraint, to improve the position tracking performance in the presence of disturbances. The proposed methods guarantee the tolerance for the position tracking error in the presence of disturbances. The proposed method consists of a high gain disturbance observer and a backstepping controller. The HGDOB is designed to estimate the disturbances which include friction, load force, and parameter uncertainties. Auxiliary state variables are proposed in order to avoid amplification of the measurement noise in HGDOB. In order to compensate for the disturbances while guaranteeing tolerance of the position tracking error, a backstepping controller using a barrier Lyapunov function (BLF) is proposed. As a result, the proposed method satisfies the output constraint in the presence of disturbances and improves the position tracking performance. In addition, the effect of input saturation caused by disturbances can be minimized using this method. The performance of the proposed methods is verified through simulations and validated through experiments. Compared to previous methods, the proposed method improves the position tracking performance.