The Application of Electrode Design in Vibrating Piezoceramic Plate for Energy Harvesting System

The energy harvesting system of piezoceramic plate is studied on the electrode configuration to improve the electromechanical transferring efficiency. The piezoceramic plate is used to perform the vibration characteristics by experimental measurements and finite element method (FEM). Thereafter, the dynamic characteristics and the electromechanical coupling efficiency of the piezoelectric energy harvesting system are studied by the electrode design method of the piezoceramic plate. Several experimental techniques are used to measure the dynamic characteristics of piezoceramic plate. First, the full-filed optical technique, amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI), can measure simultaneously the resonant frequencies and mode shapes for out-of-plane and in-plane vibrations. Second, the pointwisely measuring system, laser Doppler vibrometer (LDV), can obtain resonant frequencies by dynamic signal swept-sine analysis. Third, the correspondent in-plane resonant frequencies and anti-resonant frequencies are obtained by impedance analysis. The experimental results of vibration characteristics are verified with numerical calculations. Besides the dynamic characteristics of piezoceramic plates are analyzed in converse piezoelectric effect, the direct piezoelectric effect of piezoceramic plates are excited by shaker to generate the electric voltage. It has excellent consistence between resonant frequencies and mode shapes on the vibration characteristics by experimental measurements and finite element numerical calculations. In this study, the Electrical Potential Gradient (EPG) calculated by FEM is proposed to evaluate the electromechanical coupling efficiency of piezoceramic plate on the specific vibration mode. The correspondent electrode configuration, which is designed by EPG, can produce the best electromechanical transfer both in direct and converse piezoelectric effects. It is concluded that the vibration characteristics of piezoelectric materials have excellent consistence determined by experimental measurements and FEM.

Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach.

A Linear Matrix Inequality Solution to the Input Covariance Constraint Control Problem

In this paper, the input covariance constraint (ICC) control problem is solved by a convex optimization with linear matrix inequality (LMI) constraints. The ICC control problem is an optimal control problem that is concerned with finding the best output performance possible subject to multiple constraints on the input covariance matrices. The contribution of this paper is the characterization of the control synthesis LMIs used to solve the ICC control problem. To demonstrate the effectiveness of the proposed approach a numerical example is solved with the control synthesis LMIs. Both discrete and continuous-time problems are considered.

A Handheld Noninvasive Sensing Method for the Measurement of Compartment Pressures

Compartment syndrome is a major concern in cases of extremity trauma, which occur in over 70% of military combat casualty. Without treatment, compartment syndrome can lead to paralysis, loss of limb, or death. This paper focuses on the development of a handheld sensor that can be used for the non-invasive diagnosis of compartment syndrome. Analytical development of the sensing principle is first presented in which a relation is obtained between the pressure in a fluid compartment and the stiffness experienced by a handheld probe pushing on the compartment. Then a handheld sensor that can measure stiffness of an object without requiring the use of any inertial reference is presented. The handheld sensor consists of an array of three miniature force-sensing spring loaded pistons placed together on a probe. The center spring is chosen to be significantly stiffer than the side springs. The ratio of forces between the stiff and soft springs is proportional to the stiffness of the soft object against which the probe is pushed. Small mm-sized magnets on the pistons and magnetic field measurement chips are used to measure the forces in the individual pistons. Experimental results are presented using an in-vitro test rig that replicates a fluid pressure compartment. The sensor is shown to measure pressure accurately with a resolution of 0.1 psi over the range 0.75 psi to 2.5 psi.

Optimal Gait Design for Systems With Drift on SO(3)

Geckos that jump, cats that fall, and satellites that are inertially controlled fundamentally locomote in the same way. These systems are bodies in free flight that actively reorientate under the influence of conservation of angular momentum. We refer to such bodies as inertial systems. This work presents a novel control method for inertial systems with drift that combines geometric methods and computational control. In previous work, which focused on inertial systems starting from rest, a set of visual tools was developed that readily allowed on to design gaits. A key insight of this work was deriving coordinates, called minimum perturbation coordinates, which allowed the visual tools to be applied to the design of a wide range of motions. This paper draws upon the same insight to show that it is possible to approximately analyze the kinematic and dynamic contributions to net motion independently. This approach is novel because it uses geometric tools to support computational reduction in automatic gait generation on three-dimensional spaces.

Hybrid Powertrain Optimization With Real-Time Traffic Information

Combining hybrid powertrain optimization with traffic information has been researched before, but tradeoffs between optimality, driving-cycle sensitivity and speed of calculation have not been cohesively addressed. Optimizing hybrid powertrain with traffic can be done through iterative methods such as Dynamic Programming (DP), Stochastic-DP and Model Predictive Control, but high computation load limits their online implementation. Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS were proposed to minimize computation time, but unable to ensure real-time charge-sustaining-operation (CS) in transient traffic environment. Others show relationship between Pontryagin’s Minimum Principles (PMP) and ECMS, but iteratively solve the CS-operation problem offline. This paper proposes combining PMP’s necessary conditions for optimality, with sum-of State-Of-Charge-derivative for CS-operation. A lookup table is generated offline to interpolate linear mass-fuel-rate vs net-power-to-battery slopes to calculate the equivalence ratio for real-time implementation with predicted traffic data. Maximum fuel economy improvements of 7.2% over Rule-Based is achieved within a simulated traffic network.

Control of Autonomous Robots Using the Principles of Neuromodulation

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

Hybrid Particle Swarm: Tabu Search Optimization Algorithm for Parameter Estimation

A hybrid intelligent algorithm is proposed. The algorithm utilizes a particle swarm and a Tabu search algorithm. Swarm based algorithms and single agent based algorithms each, have distinct advantages and disadvantages. The goal of the presented work is to combine the strengths of the two different algorithms in order to achieve a more effective optimization routine. The developed hybrid algorithm is tailored such that it has the capability to adapt to the given cost function during the optimization process. The proposed algorithm is tested on a set of different benchmark problems. In addition, the hybrid algorithm is utilized for solving the estimation problem encountered for estimating the finger force output given a surface electromyogram (sEMG) signal at the input. This estimation problem is commonly encountered while developing a control system for a prosthetic hand.

Equivalency of Stability Transitions Between the SDS (Spectral Delay Space) and DS (Delay Space)

It has been shown that the stability of LTI time-delayed systems with respect to the delays can be analyzed in two equivalent domains: (i) delay space (DS) and (ii) spectral delay space (SDS). Considering a broad class of linear time-invariant time delay systems with multiple delays, the equivalency of the stability transitions along the transition boundaries is studied in both spaces. For this we follow two corresponding radial lines in DS and SDS, and prove for the first time in literature that they are equivalent. This property enables us to extract local stability transition features within the SDS without going back to the DS. The main advantage of remaining in SDS is that, one can avoid a non-linear transition from kernel hypercurves to offspring hypercurves in DS. Instead the potential stability switching curves in SDS are generated simply by stacking a finite dimensional cube called the building block (BB) along the axes. A case study is presented within the report to visualize this property.

User-Independent Hand Motion Classification With Electromyography

Electromyographic (EMG) processing is an important research area with direct applications to prosthetics, exoskeletons and human-machine interaction. Current state of the art decoding methods require intensive training on a single user before it can be utilized, and have been unable to achieve both user-independence and real-time performance. This paper presents a real-time EMG classification method which generalizes across users without requiring an additional training phase. An EMG-embedded sleeve quickly positions and records from EMG surface electrodes on six forearm muscles. An optimized decision tree classifies signals from these sensors into five distinct movements for any given user using EMG energy synergies between muscles. This method was tested on 10 healthy subjects using leave-one-out validation, resulting in an overall accuracy of 79±6.6%, with sensitivity and specificity averaging 66% and 97.6%, respectively, over all classified motions. The high specificity values demonstrate the ability to generalize across users, presenting opportunities for large-scale studies and broader accessibility to EMG-driven applications.