High-gain observer-based nonlinear control scheme for biomechanical sit to stand movement in the presence of sensory feedback delays

Abstract

Sit-to-stand movement (STS) is a mundane activity, controlled by the central-nervous-system (CNS) via a complex neurophysiological mechanism that involves coordination of limbs for successful execution. Detailed analysis and accurate simulations of STS task have significant importance in clinical intervention, rehabilitation process, and better design for assistive devices. The CNS controls STS motion by taking inputs from proprioceptors. These input signals suffer delay in transmission to CNS making movement control and coordination more complex which may lead to larger body exertion or instability. This paper deals with the problem of STS movement execution in the presence of proprioceptive feedback delays in joint position and velocity. We present a high-gain observer (HGO) based feedback linearization control technique to mimic the CNS in controlling the STS transfer. The HGO estimates immeasurable delayed states to generate input signals for feedback. The feedback linearization output control law generates the passive torques at joints to execute the STS movement. The H2 dynamic controller calculates the optimal linear gains by using physiological variables. The whole scheme is simulated in MATLAB/Simulink. The simulations illustrate physiologically improved results. The ankle, knee, and hip joint position profiles show a high correlation of 0.91, 0.97, 0.80 with the experimentally generated reference profiles. The faster observer dynamics and global boundness of controller result in compensation of delays. The low error and high correlation of simulation results demonstrate (1) the reliability and effectiveness of the proposed scheme for customization of human models and (2) highlight the fact that for detailed analysis and accurate simulations of STS movement the modeling scheme must consider nonlinearities of the system.

Nonlinearity analysis of sit-to-stand and its application

“…In addition to this, a Lyapunov-based controller was used in a linkage-based dynamic model of STS motion extracted using Lagrange's equation in the presence of sinusoidal bounded disturbances to track the desired trajectory acquired from experimental kinematic data (Nematollahi et al 2019). In two separate studies, a nonlinear control technique grounded in feedback linearization was employed to replicate the control actions of the central nervous system during the execution of STS movements (Sultan et al 2018, Sultan et al 2021.…”

Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture

“…In addition, the use of control structures such as PID does not reflect optimal neural control strategies and physiological task goals for regulating seated stability. Recently, nonlinear control theory has been used to characterize the biomechanics of postural control in the sagittal plane [35,36]. Sultan et al [35] utilized a high-gain observer along with feedback linearization to simulate the behavior of the CNS for regulating standing stability in the presence of large perturbations and neural delays.…”

“…Their results showed that the nonlinear control paradigm, along with physiological latencies, is important for understanding the behavior of the motor control process. Sultan et al [36] also investigated the use of a high-gain observer-based feedback linearization paradigm to simulate the CNS for regulating sit-to-stand transfer. They demonstrated that such control paradigm provides an accurate simulation of the underlying dynamic system and is reliable and effective for customizing human models.…”