To improve braking performance and regenerative energy of front drive electric vehicles (EVs) driven by switched reluctance motor (SRM), a regenerative braking control strategy based on multi-objective optimization of switched reluctance generator (SRG) drive system is proposed in this paper. Firstly, a partition braking force distribution strategy is developed by jointly considering braking energy and safety, and SRG drive system model is established based on low and high-speed condition. The vehicle braking system model including mechanic and regenerative braking system is built. Then, a multi-objective optimization function with three weight factors is defined, where output generated power, torque smoothness, and current smoothness are selected as optimization objectives to improve the driving range, braking comfort, and battery lifetime, respectively. Furthermore, a multi-objective optimization controller with variable switch angles is designed and combined with vehicle braking system. Finally, braking energy recovery efficiency, braking smoothness, and charging current smoothness under the multi-objective optimization controller for SRG are analyzed and compared with those under output power optimization controller. The comparison results show that the regenerative braking control strategy based on multi-objective optimization of SRG can effectively increase the vehicle braking comfort and improve battery lifetime without decreasing recovery energy.

Surface Electromyography (sEMG) has been extensively used for gait analysis and robotic control. Since the low accuracy of intention recognition limits the development of exoskeleton robots, a dynamic adaptive neural network algorithm based on the multi-feature fusion of sEMG signals was proposed to realize the accurate identification of eight lower limb movements. Firstly, the original sEMG signals were collected from eighty volunteers through gait experiments. Secondly, the feature parameters of time domain, frequency domain, and sample entropy were extracted and merged. Then, the improved differential evolution (DE) algorithm was proposed to optimize the weight values of each feature for data fusion. Thirdly, the backpropagation neural network(BPNN) used in this study is the most common model for intention recognition. However, BPNN has the problems of low accuracy and slow convergence speed, this paper proposed a novel dynamic adaptive neural network model (GA-DANN). The novel model uses genetic algorithm (GA) to optimize the weights of the BPNN to accelerate convergence. The dynamic adaptive learning rate was introduced to overcome the difficulty of determining the learning rate. The experimental results show that the proposed algorithm with multi-feature fusion information could accurately distinguish eight lower limb motion intentions, and the averaged recognition accuracy reaches 94.89%, which is 10% higher than the traditional BPNN. The averaged recognition time is only 109.67ms. This research will provide technical support for the application of rehabilitation robots.

In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by co-simulation and human experiment in accordance to track following performance and disturbances rejection ability.

The in-wheel switched reluctance motor (IWSRM) has significant potential for utilization in electric vehicle (EV) because of its inherent advantages such as low cost, robustness, wide speed range, and direct drive mode. However, the unbalanced radial force caused by eccentricity of IWSRM results in increasing vehicle vibration and decreasing riding comfort. In this paper, various vertical vibration control strategies for IWSRM are proposed and compared to improve the vibratory behavior of vehicle. First, effects of eccentricity ratio and eccentric angle on radial force, torque, and inductance are investigated by numerical analysis method for IWSRM. Then, IWSRM model, vehicle model, and suspension model are developed, based on which two common control algorithms (i.e. CCC and PWM) are provided and used as benchmarks to compare with the newly proposed two (i.e. CCC-F and PWM-F). They are designed by introducing the vehicle vertical acceleration as an extra control objective. Finally, the dynamic response of four control strategies are analyzed and the comparison results show that each of them performs differently under different driving conditions. As a result, a switchable controller based on the proposed control algorithms is presented to improve the overall performance of the IWSRM for EVs under various driving modes.

The lower extremity rehabilitation exoskeleton is mainly used to help patients with movement disorders complete rehabilitation training. For the human-machine interaction problem of the lower limb rehabilitation exoskeleton, a fuzzy radial-based impedance (RBF-FVI) controller is proposed in this study. A six degree of freedom (DOF) lower extremity rehabilitation exoskeleton was developed, and the human-machine coupling dynamics model was established. To realize the compliance control of the human-machine coupling system, a novel RBF-FVI controller is designed, which includes an inner-loop fuzzy position control module and an outer-loop impedance control module. The inner-loop fuzzy position control module is mainly used to achieve the tracking control of the desired training trajectory and position adjustment amount. The outer-loop impedance control module regulates the impedance parameters and compensates for the uncertainty terms. The superiority of the proposed controller in trajectory following is verified through simulation and comparison tests. The hardware test of the human-machine coupling system was carried out, and the test results showed that the subject and the exoskeleton system could realize a coordinated and smooth movement.

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.