Yiming Hu

The actuator reliability of Steer-by-Wire (SBW) systems is critical to the functional safety of autonomous vehicles. However, existing control methods struggle to simultaneously enhance both response speed and fault-tolerant performance when facing multiple uncertainties such as parameter perturbations, external disturbances, and actuator faults. To address these issues, this paper proposes a synergistic fault-tolerant control (FTC) strategy combining guaranteed cost control (GCC) and integral sliding mode control (ISMC). First, a dynamic model of the SBW system incorporating the multiple uncertainties is established. Second, a GCC law is derived based on linear matrix inequalities (LMIs) to impose strict constraints on the system’s tracking accuracy and robustness. Building upon this, an ISMC is integrated to significantly accelerate the system’s dynamic response without sacrificing steady-state accuracy, thereby forming a synergistic fault-tolerant architecture characterized by both high precision and rapid response. The results indicate that, under typical fault modes and steering conditions, the response speed of GCC+ISMC is significantly improved compared with GCC alone, and the GCC+ISMC reduces tracking errors by approximately 35% compared to adaptive integral sliding mode control (AISMC). These findings demonstrate that the proposed approach effectively mitigates multiple system uncertainties, offering comprehensive advantages in tracking accuracy, response speed, and robustness.

Permanent magnet synchronous motors (PMSMs) are the preferred choice for robot joint drives. Non-singular fast terminal sliding mode control (NFTSMC) can significantly enhance the robustness and control performance of PMSMs. Nevertheless, it requires the manual tuning of up to ten control parameters, and traditional tuning methods struggle to identify the optimal settings. Therefore, this paper proposes an NFTSMC optimization scheme for PMSM based on an improved gray wolf optimization (IGWO) algorithm. To ensure the GWO finds the optimal solution quickly in this application scenario, two enhancement strategies have been selected. In addition, a comprehensive evaluation indicator is proposed, which combines performance indicators such as response time and overshoot to guide the algorithm in achieving the desired control performance. Finally, an algorithm deployment scheme is proposed to achieve online performance optimization. The proposed approach has been experimentally validated using a fast-prototyping framework. The experimental results demonstrate that the proposed solution can quickly identify control parameters that meet the requirements under the guidance of the proposed evaluation indicator. Comparative experiments also confirm the superiority of the IGWO algorithm in this application.

To solve the problem of active suspension control effect reduction in trucks due to the change of vehicle mass after loading, this paper designs an active suspension hybrid controller for trucks with online estimation of vehicle mass. A multi-degree-of-freedom coupling dynamics model of the vehicle integrated with mass estimator is established, the online estimation of the vehicle mass is estimated in real time based on the recursive least squares method with forgetting factor, the mass estimation is integrated with the Linear Quadratic Regulator(LQR) of active suspension control, and the system is validated by simulation under the real-time change of mass. The results show that the active suspension hybrid controller integrated with the online estimation of the vehicle mass has a better control effect, can adapt to the real-time change of the mass of the truck and ensure the effectiveness of the control system.

Permanent magnet synchronous motors (PMSMs) play a crucial role in industrial production, and in response to the problem of PMSM turn-to-turn short-circuit and demagnetization faults affecting production safety, this paper proposes a PMSM turn-to-turn short-circuit and demagnetization fault diagnostic method based on a convolutional neural network and bidirectional long and short-term memory neural network (CNN-BiLSTM). Firstly, analyzing the PMSM turn-to-turn short-circuit and demagnetization faults, one takes the PMSM stator current as the fault signal and optimizes the variational modal decomposition (VMD) by using the Gray Wolf Optimization (GWO) algorithm in order to achieve efficient noise reduction processing of the stator current signal and improve the fault feature content in the stator current signal. Finally, the fault diagnostics are classified by using the CNN-BiLSTM, which collects advanced optimization algorithms and deep learning networks. The effectiveness of the method is verified by simulation experiment results. This scheme has high practical value and broad application prospects in the field of PMSM turn-to-turn short circuit and demagnetization fault diagnosis.

A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy.

Permanent magnet synchronous motor (PMSM) drive systems are receiving increasing attention due to their superior control quality and efficiency. Optimizing the control parameters are key to achieve the high-performance operation of PMSMs. Although heuristic algorithms demonstrate excellent optimization outcomes in simulations, there are still challenges in deploying optimization schemes in practical drives. In this study, a real-time online deployable control parameter optimization scheme is proposed. The optimization effect is evaluated through the system step response performance, and a framework for deploying optimization algorithms within the driver is developed. A fault suppression mechanism is also designed to mitigate overshoot and vibration issues caused by suboptimal solutions. The proposed scheme is validated on a rapid prototyping control platform. Experimental results confirm that the scheme exhibits good optimization performances across various operating conditions. The honey badger algorithm employed in this paper shows faster convergence and more stable optimization effects than other optimization algorithms. The optimization effect is improved by 2.2% and its performance in terms of consistency across multiple optimization results has increased by 40%.

Magnetorheological damper (MRD) has been successfully applied to vehicle suspension systems as an intelligent core component. Most conventional MRDs have closed rectangle-shaped magnetic circuits, resulting in a short effective working length and negligible damping force. To address the above issues, a novel full-channel effective MRD with trapezoidal magnetic rings (FEMRD_TMR) is proposed. The trapezoidal magnetic ring can shunt the magnetic circuit, distributing it evenly along the damping channel and increasing the effective working length. Additionally, which has the same variation trend as the magnetic flux through it, makes the magnetic induction intensity distribution more uniform to reduce the magnetic saturation problem. Theoretically analyzing the damping characteristics of the FEMRD_TMR, a quasi-static model is developed to forecast the output damping force. The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi–Sugeno (T–S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. The MRF’s yield stress is linearized into magnetic induction intensity functions by the T–S fuzzy neural network and then converted into the MRD’s structural size by the special magnetic circuit calculation. Therefore, the proposed quasi-static model can directly reflect the relationship between the damping force and structure size, simplifying MRD’s structure design. The novel quasi-static model is shown to be more straightforward and understandable than the conventional Bingham quasi-static model and to have approximately accurate damping force prediction when compared to experimental data.

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.