Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter.

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Enhancing tilt range of electrostatic torsional micromirrors using robust adaptive critic-based neurofuzzy control.

Electrostatic torsional micromirrors, as instances of Micro Electro Mechanical Systems (MEMS), have many optical network applications; such as optical wavelength-selective switches, optical cross-connects, etc. For all these applications, the micromirror needs to have minimal overshoot and settling time in order to minimize the time between two successive switching operations. Moreover, the controllability and stability of a torsional micromirror are major challenges due to high nonlinearities in its dynamic characteristics. In this paper, a robust adaptive critic-based neurofuzzy controller is proposed for electrostatic torsional micromirrors, which can improve the performance of the mirror tilting and enhance the robustness of the system to any stochastic perturbations. Furthermore, utilizing this adaptive neurofuzzy controller, which is based on a proportional and derivative (PD) critic, the micromirror "pull-in" phenomenon is crucially eliminated. Thus, the mirror tilting range is significantly expanded. Moreover, the stability of the closed-loop system is guaranteed via the Lyapunov theorem. The robust adaptive critic-based neurofuzzy controller is simulated for a 1-DOF electrostatic torsional micromirror and the results show the effectiveness of this approach for various tilt ranges and conditions. In addition, the robustness of this controller is examined in the presence of input noises and parameter uncertainties.

Linear optimal control of continuous time chaotic systems.

In this research study, chaos control of continuous time systems has been performed by using dynamic programming technique. In the first step by crossing the response orbits with a selected Poincare section and subsequently applying linear regression method, the continuous time system is converted to a discrete type. Then, by solving the Riccati equation a sub-optimal algorithm has been devised for the obtained discrete chaotic systems. In the next step, by implementing the acquired algorithm on the quantized continuous time system, the chaos has been suppressed in the Rossler and AFM systems as some case studies.

Parameter estimation and interval type-2 fuzzy sliding mode control of a z-axis MEMS gyroscope.

This paper reports a hybrid intelligent controller for application in single axis MEMS vibratory gyroscopes. First, unknown parameters of a micro gyroscope including unknown time varying angular velocity are estimated online via normalized continuous time least mean squares algorithm. Then, an additional interval type-2 fuzzy sliding mode control is incorporated in order to match the resonant frequencies and to compensate for undesired mechanical couplings. The main advantage of this control strategy is its robustness to parameters uncertainty, external disturbance and measurement noise. Consistent estimation of parameters is guaranteed and stability of the closed-loop system is proved via the Lyapunov stability theorem. Finally, numerical simulation is done in order to validate the effectiveness of the proposed method, both for a constant and time-varying angular rate.

Optimal robust control of drug delivery in cancer chemotherapy: a comparison between three control approaches.

During the drug delivery process in chemotherapy, both of the cancer cells and normal healthy cells may be killed. In this paper, three mathematical cell-kill models including log-kill hypothesis, Norton-Simon hypothesis and Emax hypothesis are considered. Three control approaches including optimal linear regulation, nonlinear optimal control based on variation of extremals and H∞-robust control based on µ-synthesis are developed. An appropriate cost function is defined such that the amount of required drug is minimized while the tumor volume is reduced. For the first time, performance of the system is investigated and compared for three control strategies; applied on three nonlinear models of the process. In additions, their efficiency is compared in the presence of model parametric uncertainties. It is observed that in the presence of model uncertainties, controller designed based on variation of extremals is more efficient than the linear regulation controller. However, H∞-robust control is more efficient in improving robust performance of the uncertain models with faster tumor reduction and minimum drug usage.

Studying the effect of kinematical pattern on the mechanical performance of paraplegic gait with reciprocating orthosis.

Paraplegic users of mechanical walking orthoses, e.g. advanced reciprocating gait orthosis (ARGO), often face high energy expenditure and extreme upper body loading during locomotion. We studied the effect of kinematical pattern on the mechanical performance of paraplegic locomotion, in search for an improved gait pattern that leads to lower muscular efforts. A three-dimensional, four segment, six-degrees-of-freedom skeletal model of the advanced reciprocating gait orthosis-assisted paraplegic locomotion was developed based on the data acquired from an experimental study on a single subject. The effect of muscles was represented by ideal joint torque generators. A response surface analysis was performed on the model to determine the impact of the kinematical parameters on the resulting muscular efforts, characterized by net joint torques. Results indicated that a lateral bending manoeuvre at the trunk would facilitate the foot clearance by reducing the torque requirement of the whole body lateral tilting. For swing leg advancement, the trunk posterior bending manoeuvre was found to be more effective and efficient than the whole body axial rotation, owing to the coupled reciprocal action of the advanced reciprocating gait orthosis. It was hypothesized that a modified gait pattern, with larger trunk movements and no axial rotation, could improve the energy expenditure and upper body loading during advanced reciprocating gait orthosis-assisted locomotion. More detailed modelling and experimental studies are needed to verify this hypothesis and evaluate its potential effects on the soft tissue strains.