Nonlinear adaptive position control of hydraulic servo system based on sliding mode back-stepping design method

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.

To mitigate the significant impact of system nonlinearities, time-varying parameters, and external load disturbances on the output force of hydraulic servo systems in active hydraulic suspensions for engineering vehicles, this study proposes a beetle swarm optimization (BSO)-optimized extended state observer (ESO)-based sliding mode control (SMC) strategy. A comprehensive mathematical model of the hydraulic servo system is established, and an ESO-based SMC controller is designed, taking into account the coupled effects of chamber pressure dynamics and external loads on the uncertain output force. The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov stability theory. The effectiveness of the proposed control strategy is verified through both numerical simulations and experimental tests. For step inputs of 5000 N and 8000 N, overshoot is significantly reduced compared with the conventional proportional–integral–derivative control and the standard extended state observer-based sliding mode control, while the settling time is shortened by more than 65% in simulations and up to 75% in experiments. Under sinusoidal force excitations at frequencies of 0.5 Hz, 1 Hz, and 2 Hz, the maximum tracking error, mean error, and standard deviation of the tracking error are substantially reduced, with the maximum error reduction exceeding 90%. These results demonstrate that the proposed method achieves high-precision force tracking under external disturbances and pronounced system uncertainties, providing an effective solution for force control of hydraulic servo systems in active suspension applications for engineering vehicles.

In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.

In this paper, we propose a Takagi-Sugeno-Kang (TSK) fuzzy model for a four-Mecanum-wheel omni-directional mobile free-base (4MW-ODMFB) with system uncertainties and external disturbances based on the model given in [12], [13], and then design an adaptive sliding-mode control (ASMC) system for trajectory tracking of 4MW-ODMFB, which combines the techniques of adaptive control and sliding-mode control. By using Lyapunov method we prove that the ASMC system is global asymptotic stable. We also conduct simulation experiments in MATLAB environment to test our TSK fuzzy model and the ASMC method. All the simulation results demonstrate the effectiveness of the proposed TSK fuzzy model and the stability and robustness of ASMC system.

Electro-hydraulic position servo system of valve-controlled symmetrical cylinder (EPSSVCS) is a very important and widely used electro-hydraulic servo system, the performance of which is often affected by friction nonlinearity and external random disturbances together with unmodeled dynamic factors such as parameter uncertainty in practical work. To improve the tracking performance of the system, a backstepping sliding mode control algorithm (BSMC) based on an extended state observer was presented in this paper. Firstly, a nonlinear mathematical model of the EPSSVCS was established which takes into account the composite disturbance induced by the systematic friction nonlinearity, external disturbances and unmodeled dynamic factors. Then, an extended state observer (ESO) was designed which can effectively estimate the velocity, acceleration, and composite disturbance of the valve-controlled cylinder online. Furthermore, a kind of BSMC was presented based on the online ESO estimates and the displacement feedback signals. The control law was given and the system stability was proved. The analysis results showed that the output signal of the system can effectively track the input signal under the influence of typical external disturbances and unmodeled dynamic factors, which illustrated the effectiveness of the established nonlinear mathematical model and the stability of the designed control system. Moreover, by taking the parameters of the electro-hydraulic servo system of a certain type of CNC machine tool as a real example, the proposed algorithm (BSMC) was compared with the conventional PID control algorithm, the backstepping sliding mode control algorithm (BSC), and the adaptive robust control algorithm (ARC), and the results verified the superiority of the BSMC. This work may provide a useful reference for the research in the field of related control systems.

An adaptive sliding-mode control system is proposed to control the position of an induction servomotor drive. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to relax the requirement for the bound of uncertainties, an adaptive sliding-mode control system is investigated to control the induction servomotor. In the adaptive sliding-mode control system a simple adaptive algorithm is utilised to estimate the bound of uncertainties. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviours of the proposed control systems are robust with regard to uncertainties.

A new adaptive fuzzy sliding-mode control (FSMC) scheme was proposed, which incorporated the fuzzy logic into sliding-mode control using fuzzy logic to approximate the equivalent control and the upper bound of uncertainty which involved the disturbance and approximation error, thus the influence of modeling error was reduced and the gain of sliding-mode control part was more fitting, such that the chattering effects could be alleviated . Besides, inspired by the thought of fully tuned RBF neural networks, a fully tuned fuzzy logic was proposed which enhanced the approximating ability of fuzzy logic system. Lyapunov stability theorem was used to prove the stability of the system and the adaptive laws were deduced. Finally, simulation results of some BTT missile were included to illustrate the effectiveness of the adaptive fuzzy sliding-mode control scheme.

We propose a novel data‐driven model‐free adaptive terminal sliding mode control (SMC) for a class of discrete nonlinear systems with perturbations in which tracking error constraints are taken into account in improving tracking accuracy. The goal of this design is that the tracking error trajectory is maintained within the prescribed convergence region which is defined by the performance function, while the disturbances exist. In explaining the advantage of this proposed control scheme, at first, by theoretical calculation, it is proved theoretically that the quasi‐sliding mode can be achieved in a limited time under the specified tracking performance. Furthermore, comparative investigation results are given to validate the effectiveness of the proposed control scheme. Simulation results show that the proposed strategy is superior to model‐free adaptive control, model‐free adaptive terminal SMC, and data‐driven adaptive SMC with prescribed performance control algorithms in terms of tracking accuracy and response speed, which demonstrates the efficiency of the proposed scheme.

In this study, the dynamic responses of an adaptive sliding-mode controlled motor-toggle servo mechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servomotor. First, a newly-designed sliding-mode controller, which is insensitive to uncertainties including parameter variations and external disturbance, is introduced. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomenon in the control effort, an adaptive sliding-mode controller is investigated to control the motor-toggle servomechanism. In the adaptive sliding-mode controller, a simple adaptive algorithm is utilized to adjust the uncertainty bounds in real-time. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed controllers are robust with regard to uncertainties.

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network for a kind of value controlled asymmetric cylinder electro-hydraulic servo system of an ammunition transfer arm in the presence of uncertain nonlinearity and parameter uncertainty. On the premise of setting the expected trajectory, using RBF neural networks to approximate unknown parameters, by setting appropriate neural network parameters and adaptive terms, the change trend of position parameter was estimated. The stability of close loop system is verified by the Lyapounov theory. Compared with the simulation results of PID control method and expected trajectory, RBF Neural Net sliding mode control method has smaller system tracking error, faster response and better tracking performance.

This paper presents a novel adaptive sliding mode control based on nonlinear sliding surface with disturbance observer (ANSMC-DOB) for precision trajectory tracking control of a surface mount technology (SMT) assembly machine. A two-degree-of-freedom model with time-varying parameter uncertainties and disturbances is built to describe the first axial mode of the pick-place actuation axis of the machine. According to the principle of variable damping ratio coefficient which makes the system have a nonovershoot transient response and a short settling time in the second-order system, the nonlinear sliding surface is designed for the sliding mode control (SMC). Since the upper bound value of the disturbances is unknown, the adaptive gain estimation is applied to replace the switching gain in the SMC. In order to settle the problem of SMC unrobust to the mismatched parameter uncertainties and disturbances, the nonlinear disturbance observer is introduced to estimate the mismatched disturbances and form the novel controller of ANSMC-DOB. The stability of sliding surfaces and control laws are verified by the Lyapunov functions. The simulation research and comparative experiments are conducted to verify the improvement of positioning accuracy and robustness by the proposed ANSMC-DOB in the SMT assembly machine.

Adaptive control has been developed for decades, and now it has become a rigorous and mature discipline which mainly focuses on dealing parametric uncertainties in control systems, especially linear parametric systems. Nonparametric uncertainties were seldom studied or addressed in the literature of adaptive control until new areas on exploring limitations and capability of feedback control emerged in recent years. Comparing with the approach of robust control to deal with parametric or nonparametric uncertainties, the approach of adaptive control can deal with relatively larger uncertainties and gain more flexibility to fit the unknown plant because adaptive control usually involves adaptive estimation algorithms which play role of “learning” in some sense. This chapter will introduce a new challenging topic on dealing with both parametric and nonparametric internal uncertainties in the same system. The existence of both two kinds of uncertainties makes it very difficult or even impossible to apply the traditional recursive identification algorithms which are designed for parametric systems. We will discuss by examples why conventional adaptive estimation and hence conventional adaptive control cannot be applied directly to deal with combination of parametric and nonparametric uncertainties. And we will also introduce basic ideas to handle the difficulties involved in the adaptive estimation problem for the system with combination of parametric and nonparametric uncertainties. Especially, we will propose and discuss a novel class of adaptive estimators, i.e. information-concentration (IC) estimators. This area is still in its infant stage, and more efforts are expected in the future for gainning comprehensive understanding to resolve challenging difficulties. Furthermore, we will give two concrete examples of semi-parametric adaptive control to demonstrate the ideas and the principles to deal with both parametric and nonparametric uncertainties in the plant. (1) In the first example, a simple first-order discrete-time nonlinear system with both kinds of internal uncertainties is investigated, where the uncertainty of non-parametric part is characterized by a Lipschitz constant L, and the nonlinearity of parametric part is characterized by an exponent index b. In this example, based on the idea of the IC estimator, we construct a unified adaptive controller in both cases of b = 1 and

The slotless self-bearing motor (SSBM) is a motor with its self-bearing function. The mechanical structure of the motor is six symmetrical hexagonal shapes. The main control problem for this motor is disturbance and uncertainty rejection. Therefore, this paper proposes a new disturbance observer (DOB) based on an optimal fixed-time state observer (OFTSOB) and adaptive sliding mode control (SMC) for the motor. Firstly, the optimal state observer was used to construct to obtain the information of the states of the bearing-less motor system. Second, a new disturbance observer base on the fast speed reaching law is proposed for estimating the unknown dynamics and unpredicted uncertainty of the motor system. Third, the adaptive fast-reaching law-sliding mode control is designed to control the positions and rotational speed. Fourth, the proposed control system is proved via the Lyapunov theorem. Finally, the corrections of proposed method once again tested by using MATLAB simulation. The obtained results figured out that the proposed method is good at rejection disturbance and uncertainty and precision in control the movement and rotation. The novelties of the proposed method are that the gains of fixed-time observer were met by the support of optimal pole placement method, the disturbances were mostly rejected by a new reaching law of unknown input observer.

In this paper, a magnetorheological (MR) mount with fuzzy sliding mode controller (FSMC) is studied for vibration suppression when the system is subject to base excitations. In recent years, magnetorheological fluids are becoming a popular material in the field of the semi-active control. However, the dynamic equation of an MR mount is highly nonlinear and it is difficult to identify. FSMC provides a simple method to achieve vibration attenuation of the nonlinear system with uncertain disturbances. This method is capable of handling the chattering problem of sliding mode control effectively and the fuzzy control rules are obtained by using the Lyapunov stability theory. The numerical simulations using one-dimension and two-dimension FSMC show effectiveness of the proposed controller for vibration suppression. Further, the well-known skyhook control scheme and an adaptive sliding mode controller are also included in the simulation for comparison with the proposed FSMC..

An effective method, adaptive fuzzy sliding-mode (SM) PI Control to design full-bridge inverter is proposed in this paper. For practical applications, sliding mode control (SMC) with chattering phenomenon is a large hindrance. The modified model of the PI controller is built by the plant extension scheme, in order to relieve the chattering. Also, the fuzzy logic is applied to find the appropriate parameter values of the PI controller, and can adapt to plant parameter variations and external load disturbances. The proposed method has been realized for the actual full-bridge inverters controlled by DSP and CPLD. Simulation and experimental results of the closed-loop system show that the proposed method yields system performance improvement, such as low total harmonic distortion (THD), fast dynamic response, reduced steady-state error, and reduced chattering.