FES rehabilitation platform with real-time control and performance feedback.

Osteoporosis after spinal cord injury is associated with low-trauma fractures, and consequently with increased risk of morbidity and mortality. The loss of bone mass density (BMD) due to paraplegia can be reduced through cyclical electrically-induced muscle contractions. Here we propose an FES control system based on posture switching, that induces transient loading of the lower limbs during a set of standing postures. This aims to produce an increased, evenly distributed BMD, whilst minimising FES-induced muscle fatigue. Here we describe the design and assessment of the FES exercising platform, comprising a controllable multi-channel electrical stimulator and an instrumented standing frame. The platform supports standing and postural shifting, provides real-time human-in-the-loop FES control with on-line feedback to the user. The platforms is used to investigate the effect of regular exercise on the distribution of BMD in people with paraplegia

Real-time failure control (SAFD)

The Real Time Failure Control program involves development of a failure detection algorithm, referred as System for Failure and Anomaly Detection (SAFD), for the Space Shuttle Main Engine (SSME). This failure detection approach is signal-based and it entails monitoring SSME measurement signals based on predetermined and computed mean values and standard deviations. Twenty four engine measurements are included in the algorithm and provisions are made to add more parameters if needed. Six major sections of research are presented: (1) SAFD algorithm development; (2) SAFD simulations; (3) Digital Transient Model failure simulation; (4) closed-loop simulation; (5) SAFD current limitations; and (6) enhancements planned for

A feedback simulation procedure for real-time control of urban drainage systems

This paper presents a feedback simulation procedure for the real-time control (RTC) of urban drainage systems (UDS) with the aim of providing accurate state evolutions to the RTC optimizer as well as illustrating the optimization performance in a virtual reality. Model predictive control (MPC) has been implemented to generate optimal solutions for the multiple objectives of UDS using a simplified conceptual model. A high-fidelity simulator InfoWorks ICM is used to carry on the simulation based on a high level detailed model of a UDS. Communication between optimizer and simulator is realized in a feedback manner, from which both the state dynamics and the optimal solutions have been implemented through realistic demonstrations. In order to validate the proposed procedure, a real pilot based on Badalona UDS has been applied as the case study.Peer ReviewedPostprint (author's final draft

Neural Feedback Scheduling of Real-Time Control Tasks

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.Comment: To appear in International Journal of Innovative Computing, Information and Contro

Stiffer optical tweezers through real-time feedback control

Using real-time re-programmable signal processing we connect acousto-optic steering and back-focal-plane interferometric position detection in optical tweezers to create a fast feedback controlled instrument. When trapping 3 µm latex beads in water we find that proportional-gain position-clamping increases the effective lateral trap stiffness ~13-fold. A theoretical power spectrum for bead fluctuations during position-clamped trapping is derived and agrees with the experimental data. The loop delay, ~19 µs in our experiment, limits the maximum achievable effective trap stiffness

Real-time feedback control of a mesoscopic superposition

We show that continuous real-time feedback can be used to track, control, and protect a mesoscopic superposition of two spatially separated wave-packets. The feedback protocol is enabled by an approximate state-estimator, and requires two continuous measurements, performed simultaneously. For nanomechanical and superconducting resonators, both measurements can be implemented by coupling the resonators to superconducting qubits.Comment: 4 pages, revtex4, 1 png figur