Anup Parikh

Automatic Control of Cycling Induced by Functional Electrical Stimulation With Electric Motor Assistance

Cycling induced by automatic control of functional electrical stimulation provides a means of therapeutic exercise and functional restoration for people affected by paralysis. During cycling induced by functional electrical stimulation, various muscle groups are stimulated according to the cycle crank angle; however, because of kinematic constraints on the cycle-rider system, stimulation is typically only applied in a subsection of the crank cycle. Therefore, these systems can be considered as switched control systems with autonomous, state-dependent switching with potentially unstable modes. Previous studies have included an electric motor in the system to provide additional control authority, but no studies have considered the effects of switched control in the stability analysis of the motorized functional electrical stimulation cycling system. In this paper, a model of the motorized cycle-rider system with functional electrical stimulation is developed that includes the effects of a switched control input. A novel switching strategy for the electric motor is designed to only provide assistance in the regions of the crank cycle where the kinematic effectiveness of the riders muscles is low. A switched sliding-mode controller is designed, and global, exponentially stable tracking of a desired crank trajectory is guaranteed via Lyapunov methods for switched systems, despite parametric uncertainty in the nonlinear model and unknown, time-varying disturbances. Experimental results from five able-bodied, passive riders are presented to validate the control design, and the developed control system achieves an average cadence tracking error of

Homography based visual servo control with scene reconstruction

0.00\pm 2.91

Compensating for changing muscle geometry of the biceps brachii during neuromuscular electrical stimulation: A switched systems approach

revolutions per minute for a desired trajectory of 50 revolutions per minute.

Adaptive control of a surface marine craft with parameter identification using integral concurrent learning

Homography based visual servoing is an approach that blends image based feedback with feedback that is reconstructed from the image to control an autonomous system to move along a desired trajectory. Adaptive control methods have been previously developed by compensating for an unknown parameter (i.e., the depth of a feature) in the dynamics, where persistence of excitation assumptions are used for parameter identification. Rather than assume persistent excitation, an augmented adaptive update law that uses recorded data is utilized in this paper to guarantee exponential tracking and parameter identification with only finite excitation. By identifying the depth parameter, the structure of the scene can be reconstructed, enabling simultaneous mapping and control.

Compensating for uncertain time-varying delayed muscle response in isometric neuromuscular electrical stimulation control

Functional electrical stimulation (FES) is commonly used in rehabilitation therapy for people with injuries or various neurological disorders. Noninvasive treatments use surface electrodes to provide a potential field across the muscle and induce contractions/output force. The placement of the electrodes has a significant impact on the induced force output. As the muscle geometry changes (i.e., muscle lengthening or shortening), the force induced by the static electrode placement may also change. In this paper, an array of electrodes is placed across the biceps brachii and the electric field is switched across the electrodes (i.e., channels) to maximize the induced muscle force throughout the arms range of motion, despite changes in the muscle geometry. To yield this outcome, a switched systems approach is used to develop a position-based switching law for the uncertain nonlinear system. Specifically, a switched robust sliding mode controller is developed to track a desired angular trajectory about the elbow. Lyapunov-based methods for switched systems are used to prove global exponential tracking and experimental results demonstrate the performance of the switched control system.

A switched systems approach to vision-based localization of a target with intermittent measurements

A controller is developed for a three degrees-of-freedom surface marine craft where both the rigid body and hydrodynamic parameters are unknown. A Lyapunov-based analysis is presented to show the closed loop system is globally exponentially stable and the uncertain parameters are identified exponentially without the requirement of persistence of excitation. Simulation results are provided to validate the theory and demonstrate performance.

A switched systems approach to image-based localization of targets that temporarily leave the field of view

Recent results indicate that muscles have a delayed response to neuromuscular electrical stimulation (NMES). Muscle groups are known to rapidly fatigue in response to external muscle stimulation when compared to volitional contractions, and recent results indicate that this input delay increases as the muscle fatigues. Since the exact value of the time-varying input delay is difficult to measure during feedback control, the uncertain input delay presents a significant challenge to designing controllers for force tracking during isometric NMES. In the present work, a continuous robust controller is developed that compensates for the uncertain time-varying input delay for the uncertain, nonlinear NMES dynamics of the lower limb despite additive bounded disturbances. A Lyapunov-based stability analysis is used to prove that the error signals are uniformly ultimately bounded.

A Switched Systems Framework for Guaranteed Convergence of Image-Based Observers With Intermittent Measurements

Switched systems theory is used to analyze the stability of vision-based observers for 3D localization of objects in a scene. Observers or filters that are exponentially stable under persistent observability may have unbounded error growth under intermittent sensing loss, even while providing seemingly accurate state estimates. Therefore, conditions are developed based on an average dwell time criteria to guarantee state error convergence with a known decay rate. In cases where sensing is controllable, these conditions relax path constraints for visual servoing applications. The conditions are developed in a general form, applicable to any exponentially stable observer, and utilize object motion knowledge to maximize the allowable time spent in stabilizable, but unobservable, periods.

A Switched Systems Approach Based on Changing Muscle Geometry of the Biceps Brachii During Functional Electrical Stimulation

Vision sensors provide rich information about the environment and can be utilized for tracking the position of moving objects from a moving camera. This paper presents the development of dwell time conditions to guarantee convergence to an ultimate bound of state estimation errors for a class of vision based observers with intermittent measurements. Bounds are developed on the unstable growth of the estimation errors during the periods when the object being tracked is not visible. A Lyapunov analysis for the switched system is performed to develop an inequality in terms of the duration of time the observer can view the moving object and the duration of time the object is out of the field of view.

Rapid abstract perception to enable tactical unmanned system operations

Switched systems theory is used to analyze the stability of image-based observers for three-dimensional localization of objects in a scene in the presence of intermittent measurements due to occlusions, feature tracking losses, or a limited camera field of view, for example. Generally, observers or filters that are exponentially stable under persistent measurement availability may have unbounded error growth under intermittent measurement loss, even while providing seemingly accurate state estimates. By constructing a framework that utilizes a state predictor during periods when measurements are not available, a class of image-based observers is shown to be exponentially convergent in the presence of intermittent measurements if an average dwell time, and a total unmeasurability time, condition is satisfied. The conditions are developed in a general form, applicable to any observer that is exponentially convergent assuming persistent visibility, and utilizes object motion knowledge to reduce the amount of time measurements must be available to maintain convergence guarantees. Based on the stability results, simulations are provided to show improved performance compared to a zero-order hold approach, where state estimates are held constant when measurements are not available. Experimental results are also included to verify the theoretical results, to demonstrate applicability of the developed observer and predictor design, and to compare against a typical approach using an extended Kalman filter.