For the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control (ANNNFTSMC) method is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the nonsingular fast terminal sliding mode control (NFTSMC) can avoid the singularity problem and make the state of the system converge to the equilibrium point quickly, so as to improve the response speed of the system. Secondly, in order to minimize the influence of disturbance and dynamic uncertainty, the dynamic model of PMLSM servo system is estimated by RBF neural network, and the uncertain upper bound of PMLSM servo system is estimated in real time combined with adaptive control, which weakens the chattering phenomenon and enhances the robustness of the system. It is proved theoretically that the control scheme can make the system achieve fast convergence and good tracking. Finally, the system experiments show that the proposed control scheme has the advantages of high tracking accuracy, good robustness, fast response speed and small position error. INDEX TERMS Permanent magnet linear synchronous motor, nonsingular fast terminal sliding mode control, adaptive, RBF neural network.

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In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

In this study, a modified complementary sliding mode control (MCSMC) method based on a disturbance force observer with mass identification (DFOB‐MI) applicable to the permanent magnet linear synchronous motor is proposed to achieve high‐performance servo control fields. MCSMC is an improvement on the complementary sliding mode control (CSMC) method, which incorporates an approach angle into the saturation function. MCSMC allows for asymptotic convergence of the position tracking errors and guarantees the global robustness of the system. In addition, compared to hybrid control strategies combining neural networks with CSMC, the MCSMC method has a simpler structure and faster response. However, in practical applications, the mass variation of the mover has a significant impact on system performance. To achieve better dynamic and static characteristics, a disturbance force observer capable of identifying the mass variation based on model reference adaptive identification theory is proposed. DFOB‐MI can identify the mass of the mover and provide information on the disturbance caused by the change of the load. Thus, the compensation current is calculated to reduce the disturbance and realise compensation. The more accurate tracking performance and stronger robustness of the proposed control scheme compared to conventional approaches have been confirmed through comparative experimental studies.

The permanent magnet linear servo system is usually susceptible to uncertainties, such as parameter variations, external disturbances, and friction forces. To address this problem, a complementary sliding mode control (CSMC) via Elman neural network (ENN) was presented in this paper. First, the mathematical model of the permanent magnet linear synchronous motor (PMLSM) with a lumped uncertainty was established. Second, on the basis of the traditional sliding mode control (SMC), CSMC was designed by combining the integral sliding surface with the complementary sliding surface. CSMC is generally used to reduce the chattering phenomenon and, consequently, to improve the tracking performance. However, the values of the switching gain and the boundary layer thickness are difficult to select in CSMC. To deal with this problem, ENN was adopted in the proposed CSMC system to replace the switching control law. Due to its strong learning ability, ENN can estimate the value of the lumped uncertainty and adjust the parameters online, thus further improving the robustness of the system. In addition, to verify the control performance of the proposed method, a digital signal processor (DSP) was implemented as the experimental platform to control the mover of the PMLSM for the tracking of different reference trajectories. The experimental results show that the proposed control strategy not only improves tracking accuracy but also guarantees the robustness of the system.

In this paper, aiming at the known or unknown uncertainties in permanent magnet linear synchronous motor (PMLSM) servo system, an intelligent backstepping terminal sliding mode control (IBTSMC) method is proposed for accurate position tracking of the servo system. The designed controller makes use of the advantage of backstepping terminal sliding mode control theory to ensure fast convergence and robustness. Finally, the stability analysis is carried out by using Lyapunov stability theory, and the effectiveness of the designed control scheme is proved by experiments.

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In this paper, an approach angle-based saturation function of modified complementary sliding mode control (MCMSC) method was proposed to overcome the influence of uncertainties such as parameter variations and external disturbances on permanent magnet linear synchronous motor (PMLSM) servo system. On the foundation of the mathematical model considering uncertainties of PMLSM and the theory of sliding mode control (SMC), complementary sliding mode control (CSMC) method was designed by combining the integral sliding surface with the complementary sliding surface. By using the continuous saturation function, CSMC can efficiently eliminate the system chattering phenomenon caused by the discontinuous function in SMC and further improve the position tracking accuracy. However, the saturation function makes the boundary layer in CSMC constant, so the asymptotic stability of the system cannot be guaranteed. Hence, an approach angle-based saturation function of MCSMC was designed to realize the dynamic change of the boundary layer, which can diminish the boundary layer with the change of state trajectory until it converges to the sliding surface, thereby further improving the robustness of the system. Additionally, the hybrid SMC methods, such as fuzzy-SMC and neural network-SMC (NN-SMC), are avoided. A precise test platform based on digital signal processor (DSP) was implemented, and experimental results are shown to demonstrate the effectiveness and correctness of the proposed method.INDEX TERMS Permanent magnet linear synchronous motor (PMLSM), uncertainties, complementary sliding mode control (CSMC), saturation function, approach angle.