In existing sliding mode control (SMC) methods, a disturbance observer (DOB) is employed to achieve disturbance rejection and reduce chattering, which is a major disadvantage associated with SMC and increases with increasing switching gain. However, the disturbance in a DOB associated with SMC is typically estimated using a control input that exhibits chattering. Although the existing methods can reduce chattering using SMCs with DOBs, the estimated disturbance comprises undesirable high-frequency components caused by the control input. In this article, a chattering attenuation disturbance observer (CADOB) is proposed to achieve chattering reduction in the SMC. A frequency spectrum analysis is performed for the control input, estimated disturbance, and controlled state to elucidate the effect of chattering in the control input on the estimated disturbance and controlled state obtained using existing methods. The proposed CADOB is designed to estimate the disturbance with a reduction in undesirable high-frequency components. The frequency response results demonstrate that the effect of chattering in the control input on the estimated disturbance can be reduced by using CADOB, while maintaining the disturbance estimation performance. Moreover, the performance of the CADOB is experimentally validated using SMC in permanent-magnet synchronous motors.

In this study, an adaptive neural network (NN) control using nonlinear information (NI) gain for permanent magnet synchronous motors (PMSMs) is proposed to improve control and estimation performance. The proposed method consists of a nonlinear controller, a three-layer NN approximator, and NI gain. The nonlinear controller is designed via a backstepping procedure for position tracking. The commutation scheme is designed to implement the PMSM control without the direct-quadrature (DQ) transform. The three-layer NN approximator is designed to estimate the unknown complex function generated by the recursive backstepping process. The NI gains are designed to enhance the control and estimation performance according to the increased tracking errors owing to the load torque and the desired position variations. All of signals in the closed-loop system guarantee the semiglobal uniformly ultimately boundness (UUB) using the Lyapunov stability theorem and the input-to-state stability (ISS) property. The performance of the proposed method was validated by experiments performed using a PMSM testbed.

In this article, we propose the fuzzy event-triggered super twisting sliding mode control (STSMC) approach for improving the position tracking performance and reducing the communication resource under various unknown disturbances in the control system of permanent magnet synchronous motors (PMSMs). The event-triggered strategy is designed to minimize the unnecessary use of computation/communication resources while achieving acceptable control performances. However, when the unexpected disturbances are injected, the tracking performance could be degraded since the conventional event-triggered strategy using previous tracking error information cannot immediately update the control input according to rapid unexpected disturbances. To resolve this problem, we design the fuzzy rule, which is applied in the event-triggered strategy to adjust the updating time based on the tracking error at the present time to achieve robustness against unexpected disturbances. Thus, a fuzzy event-triggered control input can be transmitted immediately to the PMSM when an unexpected disturbance occurs. The STSMC method is designed to enhance the position tracking performance under disturbances. The stability of the closed-loop is shown using the modified Lyapunov theory. In addition, the nonexistence of Zeno execution is guaranteed by analyzing the minimum bounded event-triggered interval time. The effectiveness of the proposed method was verified through simulations and experiments, where the experiment was conducted using a PMSM testbed.

In this paper, a robust nonlinear torque control method using the steering wheel torque (SWT) model is proposed for electric power steering (EPS) systems. We develop an EPS model using SWT as the state variable to directly control the SWT. A robust nonlinear torque control method is designed to improve the SWT control performance of EPS. The proposed method consists of an extended state observer and a nonlinear sliding mode control (SMC) method. The extended state observer is designed to estimate the lumped disturbance, which includes modeling errors and external disturbances for robustness. The SMC is designed to track the desired SWT and suppress the estimation error. The nonlinear gain is proposed for reducing the chattering in the SMC. The proposed method is designed based on the SWT model; thus, it can improve the torque control performance while remaining robust to parameter uncertainty and external disturbances. The performance of the proposed method is validated via experiments using a pinion type EPS hardware-in-the-loop simulation.

In this paper, we propose a fixed-time slip control with an extended-state observer (ESO) using only wheel speed for anti-lock brake systems (ABS) in electric vehicles. In the proposed control strategy, the longitudinal tire-road friction is defined as the disturbance. The ESO is designed to estimate the wheel speed, longitudinal tire-road friction, and longitudinal vehicle speed. Based on the estimated longitudinal tire-road friction, an optimal desired slip generator is designed, which provides optimal desired slip in real-time to maintain a nearly maximum tire-road friction coefficient (TFC), according to road surface conditions. A fixed-time controller is developed to guarantee an upper bound of convergence time determined regardless of the initial slip tracking errors. The proposed method guarantees a uniform ultimate boundedness of the wheel-slip tracking error based on an analysis of closed-loop stability. The performance of the proposed method is validated via simulations by using CarSim and MATLAB/Simulink for three scenarios on a jump TFC road surface, on a split TFC road surface, and on a suddenly cornering jump TFC road surface. The proposed method, which only employs a wheel speed sensor, achieves a shorter braking distance and stopping time in comparison with classical ABS.

In practice, steering wheel torque (SWT) control performance for an electric power steering (EPS) system may degrade owing to various reasons, such as model/parameter uncertainties and unpredictable large external disturbances (e.g., reaction force). Furthermore, the desired SWT suddenly varies and its sign also changes because the direction of the steering wheel angle (SWA) often changes according to the driver. In this article, a neural network (NN) approximation-based adaptive nonlinear control (ANC) using reinforced gain (RG) for an EPS system is proposed to resolve the above-mentioned problems using an SWT model. The proposed control method employs an NN approximator, ANC, and RG. The NN approximator is designed to estimate the unknown complex nonlinear function in EPS modeling. The ANC is designed via backstepping to compensate for parametric uncertainties and external disturbances. Further, the RG is designed as a positive nonlinear function of the error to sufficiently suppress the error according to variations in the desired SWT and external disturbance. The RG increases to suppress the error at a rapid transient response, owing to the sudden change in the sign of the desired SWT and the external disturbance. Thus, a high gain (increased RG) is used only when necessary so that an unnecessarily high gain can be avoided.