3 sampled-data control of nonlinear systems

This chapter provides some of the main ideas resulting from recent developments in sampled-data control of nonlinear systems. We have tried to bring the basic parts of the new developments within the comfortable grasp of graduate students. Instead of presenting the more general results that are available in the literature, we opted to present their less general versions that are easier to understand and whose proofs are easier to follow. We note that some of the proofs we present have not appeared in the literature in this simplified form. Hence, we believe that this chapter will serve as an important reference for students and researchers that are willing to learn about this area of research

A note on input-to-state stabilization for nonlinear sampled-data systems

We provide a framework for the design of stabilizing controllers via approximate discrete-time models for sampled-data nonlinear systems with disturbances. In particular, we present sufficient conditions under which a discrete-time controller that input-to-state stabilizes an approximate discrete-time model of a nonlinear plant with disturbances would also input-to-state stabilize (in an appropriate sense) the exact discrete-time plant model

Mechanical design of an affordable adaptive gravity balanced orthosis for upper limb stroke rehabilitation

In this paper, a novel design of a non-powered orthosis for upper limb stroke rehabilitation is reported. Its design exploits the gravity balancing theory. Designed for home-based use, it is the first affordable, passive design to incorporate an assistive level that can be adaptively varied within a closed-loop control scheme. This allows the device to be integrated with a dual robotic and electrical stimulation control scheme, to thereby enable full exploitation of the motor relearning principles which underpin both robotic therapy and Functional Electrical Stimulation (FES) based stroke rehabilitation. This embeds the potential for more effective treatment. The article focuses on the mechanical design of the non-powered orthosis, providing detailed design, dynamic analysis and evaluation. Publisher statement: “This is an Accepted Manuscript of an article published by Taylor & Francis in Mechanics Based Design of Structures and Machines on 14 June 2015, available online: http://www.tandfonline.com/10.1080/15397734.2015.1054513

Repetitive control of electrical stimulation for tremor suppression

Tremor is a rapid uncontrollable back-and-forth movement of a body part often seen in patients with neurological conditions such as Multiple Sclerosis (MS) and Parkinson’s disease. This debilitating oscillation can be suppressed by applying functional electrical stimulation (FES) within a closedloop control system. However current implementations use classical control methods and have proved capable of only limited performance. This paper develops a novel application of repetitive control (RC) that exploits the capability of learning from experience to enable complete suppression of the tremor. The proposed control structure is applied to suppress tremor at the wrist via FES regulated co-contraction of wrist extensors/flexors. Experimental evaluation is performed using a validated wristrig and results are compared against classical feedback control designs to establish the efficacy of the approach

System identification for FES-based tremor suppression

Tremor is an involuntary motion which is a common complication of Parkinson's disease and Multiple Sclerosis. A promising treatment is to artificially contract the muscle through application of induced electrical stimulation. However, existing controllers have either provided only modest levels of suppression or have been applied only in simulation. To enable more advanced, model-based control schemes, an accurate model of the relevant limb dynamics is required, together with identification procedures that are suitable for clinical application. This paper proposes such a solution, explicitly addressing limitations of existing methodologies. These include model structures that (i) neglect critical features, and (ii) restrict the range of admissible control schemes, together with identification procedures that (iii) employ stimulation inputs that are uncomfortable for patients, (iv) are overly complex and time-consuming for clinical use, and (v) cannot be automated. Experimental results confirm the efficacy of the proposed identification procedures, and show that high levels of accuracy can be achieved in a short identification time using test procedures that are suitable for future transference to the clinical domain

Teaching embedded control system design of electromechanical devices using a lab-scale smart farming system

This paper presents the design and monitoring of a lab-scale smart farming system through the integration of control and app designs that can be used for teaching embedded control application to electromechanical systems. A combination of sensors and actuators is used to develop an Arduino based embedded feedback control system that could be implemented in a smart farming environment. Specifically, we look at controlling electromechanical devices to actuate the fan and water pump to provide the optimal temperature and moisture, respectively, to enhance plant growth in a smart farming setting. The effectiveness of the feedback control is tested by conducting a plant growth experiment. Using garden cress (Lepidium sativum) as a case study, the plant grown in the controlled temperature and moisture settings shows substantially healthier growth compared to the one grown in the non-controlled environment. In addition, an app is designed and developed to transform the Arduino data stream from the sensors into valuable insights that could help the users to monitor and improve the overall crop health. The developed system in this paper enables students to learn integral skills from interdisciplinary engineering fields (e.g., systems, control, mechanical and computer) to solve an agricultural problem

Acoustofluidic particle steering

Steering micro-objects using acoustic radiation forces is challenging for several reasons: Resonators tend to create fixed force distributions that depend primarily on device geometry, and even when using switching schemes, the forces are hard to predict a priori. In this paper an active approach is developed that measures forces from a range of acoustic resonances during manipulation using a computer controlled feedback loop based in matlab, with a microscope camera for particle imaging. The arrangement uses a planar resonator where the axial radiation force is used to hold particles within a levitation plane. Manipulation is achieved by summing the levitation frequency with an algorithmically chosen second resonance frequency, which creates lateral forces derived from gradients in the kinetic energy density of the acoustic field. Apart from identifying likely resonances, the system does not require a priori knowledge of the structure of the acoustic force field created by each resonance. Manipulation of 10 μm microbeads is demonstrated over 100 s μm. Manipulation times are of order 10 s for paths of 200 μm length. The microfluidic device used in this work is a rectangular glass capillary with a 6 mm wide and 300 μm high fluid chamber.</p

Real Time Bridge Dynamic Response: Bridge Condition Assessment and Early Warning System

Present study investigating the use of wireless sensor networks (WSNs) in the assessment of bridge condition as well as early warning system. The WSNs are used to measure the acceleration occurred on the bridge and the mode shape of the bridge as the excitation loads passing through the bridge. Fast Fourier Transform (FFT) is applied to transform the measured acceleration to get the frequency of the bridge dynamic response. Numerical integration is applied to determined the acceleration to get the displacement of the bridge dynamic response.  Implementing structural dynamics equation, the effective stiffness of the bridge can be determined using the frequency. The effective stiffness and the bridge dynamic response are then used to obtained the bridge condition and load ratings. A scaled model of steel truss bridge and miniature truck with various loads were used to simulate the use of WSNs in bridge assessment, which were also used to validate the finite element model. The finite element model was then used to simulate various scenarios, including the scenarios in which the bridge elements had various level of damages. The behaviors of bridge with various level of damages can be used to identify the location and the level of damages in the bridge and were found to be useful as early warning system for bridges condition and load ratings