Adaptive Neural Control for Gait Coordination of a Lower Limb Prosthesis

Because of distinct differences in structure and drive, the lower limb amputee that walks with prosthesis forms a heterogeneous coupled dynamic system. The strongly coupled nonlinearity makes it difficult for the lower limb prosthesis (LLP) to adapt to complex tasks, such as variable-speed walking and obstacle crossing. As a result, the typical behavior can be seen as gait incoordination or even gait instability. This paper proposes a new gaitcoordination-oriented adaptive neural sliding mode control (GC-ANSMC) for the heterogeneous coupled dynamic system. At the high level, the controller adopts the homotopy algorithm, which inherits the intelligence of the healthy lower limb (HLL), to create the GC-oriented desired trajectory for the LLP. The embedding parameter of the homotopy algorithm is updated online based on the mean difference between the lab-based target trajectory and the HLL's delayed motion, resulting in better GC performance. In addition, the new GC-planning strategy has sufficient environmental adaptability with a limited lab-based target trajectory for complex tasks. At the low level, radial basis function neural network (RBFNN) is employed to model the human-prosthesis heterogeneous coupled system uncertainties online and generate the controlled torques for simultaneous uncertainty compensation and gait driving. According to Lyapunov's theory, the sliding mode gains and the cubic order evolution rules of the network's weight are carried out. As a result, the global convergence of the proposed control approach can be ensured, and the dynamic motion could be quickly tracked. Applications for the variable-speed walking and the obstacle crossing show that the present GC-ANSMC could achieve better control accuracy, faster convergence speed, lower controlled torques, and higher GC performance than traditional methods. These advantages, as a result, indicate a convincing potential for the adaptive control for the nonlinear human-prosthesis heterogeneous coupled dynamics in complex tasks.

Automated summary

This paper proposes a new gait coordination-oriented adaptive neural sliding mode control (GC-ANSMC) for lower limb amputees using prostheses, addressing the difficulties in adapting to complex tasks. The approach combines a homotopy algorithm for trajectory generation with a radial basis function neural network for modeling uncertainties, achieving fast motion tracking and global convergence. Applications show that GC-ANSMC outperforms traditional methods in control accuracy, convergence speed, torque control, and gait coordination performance, demonstrating promising potential for adaptive control in nonlinear human-prosthesis dynamics.