With the rapid development of rehabilitation robotics, there is a pressing need for efficient and accurate gait prediction methods. However, due to the complexity and variability of individual gait characteristics and external disturbances, accurately predicting gait in real time remains a significant challenge. This paper proposes an innovative Bayesian-inference-based method for real-time gait prediction while a subject walks with a lower-limb exoskeleton. Periodic gait information is represented using von Mises basis functions, and the weight parameters serve as real-time updated state variables. The error-subspace transform Kalman filter (ESTKF) is applied for gait trajectory prediction. A fully connected neural network (FCNN) is used to estimate the walking speeds in real time based on predicted trajectories. Comparative experiments based on an open-source database prove the advantages of ESKTF compared with other Bayesian filters. Walking experiments are conducted to estimate phase and speed in real time, and to predict the joint angle, total joint torque, and lower-limb muscle surface electromyography (sEMG) values. Experimental results validate the method's prediction performance across different speeds and demonstrate its resilience to external interference.

In this paper, a fault-tolerant finite control set model predictive current control (FCS-MPCC) method based on the extended voltage vector and adaptive control set (FCS-MPCC-EACS) is proposed, which improves the fault-tolerant control accuracy and stability of five-phase permanent magnet synchronous motor (F-PMSM) under two-phase open-circuit fault (OCF), and reduces the computational burden. Firstly, the extended voltage vector is proposed, and the optimal voltage vector and duty ratio are calculated by optimizing the cost function, thereby improving the fault-tolerant control accuracy and stability of the F-PMSM. Then, the deadbeat (DB) method is utilized to calculate the reference voltage vector for constructing the adaptive control set, which replaces the complete voltage vector enumeration process, thereby reducing the computational burden. Simulation test results show that compared with the FCS-MPCC method, FCS-MPCC with the extended voltage vector control set (FCS-MPCC-ECS) and FCS-MPCC-EACS methods improve the speed fault-tolerant control accuracy under non-adjacent two-phase OCF by 14.9% and 17.4%, respectively. At the same time, compared with the FCS-MPCC-ECS method, the calculation time of the FCS-MPCC-EACS method is shortened by 83.2%.

Due to low computational burden, high control bandwidth, excellent dynamic performance, and no overshoot, the deadbeat model predictive current control (DB-MPCC) method has great application potential in five-phase permanent magnet synchronous motors (F-PMSMs). However, the high dependence on the accuracy of the prediction model seriously affects the practical performance and application of the DB-MPCC method. This paper proposes an improved incremental model based DB-MPCC (IIM-DB-MPCC) method for F-PMSMs, which improves the discretization accuracy of the prediction model and effectively reduces the dependence of the DB-MPCC method on the accuracy of the prediction model. Firstly, the prediction model of the F-PMSM is discretized using the zero-order holding (ZOH) method, and the quantized control of the model discretization error is realized. Then, based on the improved discrete prediction model, the IIM-DB-MPCC method is proposed. Simulation test results show that, compared with the DB-MPCC method, the IIM-DB-MPCC method proposed in this paper can obtain higher prediction model accuracy than the Euler method at a low computational burden cost. Moreover, the static error caused by the mismatch of prediction model parameters is eliminated and the current and speed control accuracy of the F-PMSM is improved.