Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

Abstract

Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review

“…Classical linear control methods require a high understanding of the dynamics of the mechanical components, such as with Hongsheng et al (2021) in Figure 7A, where the pneumatic actuators are controlled by a series of highly complex equations with changes depending on the expected movement and the lengths of the bars, creating the problem of a great method of control for a very specific task, which is not the case for everyday use. By using a hybrid model seen in Figure 7B, combining the inertia matrix (M(θ), Coriolis and centripetal values (C(θ)), gravitational force vector (G(θ)), and a fuzzy neural network to estimate the time estimation values, Peng et al (2020a) were capable of calculating the required torque and angle in the knee movement capable of handling disturbances affecting the swing trajectory.…”

“…For locomotion prediction, as seen with Welker et al (2021), humans can adapt to different techniques of manual selection, showing less than 8% in the error between expected versus actual position on ankle angle. An improvement can be seen with Peng et al (2020a), in which by using autonomous locomotion prediction, the user does not have to emulate the same movement with another limb nor specify a manual change to the desired terrain. The results of these experiments show a high percentage of accuracy (more than 80% on most of the predictions), with the problem appearing when defining an initial certainty value of the possible next terrain, in which the manually selected values can increase detection errors (Stairs Down locomotion mode, with 67% of detection rate).…”

“… • Gait : Refers to the movements performed inside the locomotion, the series of steps to perform an actual step in any locomotion type, usually composed of the swing and stance phases, with the transition between them, such as “heel on the ground, heel off the ground, toe on the ground, and toe off the ground” ( Filho et al., 2021 ). These movements and the speed depending on the user’s intention have been tracked by BCIs, EMGs, and IMUs to get a more natural feeling of walking in different terrains ( Gao et al., 2021 ), but this type of research uses Finite State Machines (FSMs) to focus on the whole trajectory tracking to minimise the studies on the dynamics of the movement ( Mai and Commuri, 2016 ; Whitmore et al., 2016 ; Cao et al., 2018 ; Adamczyk, 2020 ; Peng et al., 2020a ; Mendez et al., 2020 ; Shafiul Hasan et al., 2020 ; Sutawika et al., 2021 ; Welker et al., 2021 ; Yokoyama et al., 2021 ). Since the movement is a whole complex trajectory, instead of specific points to be reached by a single actuator, the schemes that are used must be robust against disturbances, and, in the case of multiple actuators, actions must be performed at coordinated times.…”

Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint

“…Scandaroli [21] introduced adaptive control to the swing control of MR prostheses and proposed a model reference adaptive control (MRAC) algorithm, the principle of which is to design an appropriate adaptive law to estimate the model parameters and adjust the controller output to make it follow the desired trajectory. To solve the problem of parameter uncertainty and strong coupling, Fang et al [22] devised an adaptive robust force/position control algorithm that makes use of time delay estimation technology, sliding mode control and a fuzzy neural network to achieve finite-time convergence and gait tracking. The simulation results indicate that this strategy has better trajectory tracking characteristics and strong robustness in the presence of unknown external interference.…”

Design and Modeling of Series-Parallel Compliant Device for Reliable Assembly Under Position/Angle Deviation

“…One is the active compliant strategy, which uses force feedback to identify the misalignment and compensates the positioning error using the feedback control. In the active compliance strategy, many control strategies have been presented to improve the assembly performance, including the stiffness control [3][4], the impedance control [5][6][7], and the force/position hybrid control [8][9][10][11][12]. Although the active compliance strategy has gained many successful applications, its complex control algorithm is often challenging to implement.…”