Siddhartha S. Mehta

Robust nonlinear aircraft tracking control using synthetic jet actuators

A robust, nonlinear tracking control strategy is presented for an aircraft equipped with synthetic jet actuators (SJA). The control law is designed to be easily implementable, requiring no observers, function approximators, or adaptive laws. By exploiting minimal knowledge of the structure of the nonlinear SJA dynamic model, a matrix decomposition technique is exploited to compensate for the input-multiplicative parametric uncertainty inherent in the SJA dynamics. The control law is shown to yield global asymptotic trajectory tracking in the presence of parametric uncertainty, actuator nonlinearity, and unknown, nonlinear external disturbances. A rigorous Lyapunov-based stability analysis is utilized to prove the theoretical result, and numerical simulation results are provided to demonstrate the performance of the proposed control law.

Daisy Chaining Based Visual Servo Control Part I: Adaptive Quaternion-Based Tracking Control

A quaternion-based visual servo tracking controller for a moving six degrees of freedom object is developed based on image feedback from a moving camera. The control objective is for the object to track a desired trajectory determined by a sequence of prerecorded images from a stationary camera. To achieve this result, several technical issues were resolved including: discriminating the relative velocity between the moving camera and the moving object, compensating for the unknown time-varying distance measurement from the camera to the object, relating the unknown attitude of the control object to some measurable signals, and using unit quaternion to formulate the rotation motion and rotation error system. By utilizing multi-view image geometry to develop homography relationships between the moving camera frame and the moving planar patch coordinate systems, the relative velocity issue is resolved. By using the depth ratios obtained from the homography decomposition, the unknown depth information is related to an unknown constant that can be compensated for by a Lyapunov-based adaptive update law. Lyapunov-based methods are provided to prove the adaptive asymptotic tracking result.

Adaptive Vision-Based Collaborative Tracking Control of An UGV via a Moving Airborne Camera: A Daisy Chaining Approach

A cooperative visual servo tracking controller is developed in this paper with the objective to enable an unmanned ground vehicle (UGV) to follow a desired trajectory encoded as a sequence of images (i.e. a prerecorded video) utilizing the image feedback from a moving airborne monocular camera system. An innovative daisy chaining strategy is used to resolve the relative velocity between the moving airborne camera (i.e., a camera attached to a remotely piloted aircraft), the moving UGV, and the desired time varying UGV trajectory. An adaptive Lyapunov-based control strategy is employed to actively compensate for the lack of known depth measurements and the lack of an object model

Nonlinear Control of Hypersonic Missiles for Maximum Target Penetration

The design of guidance and control laws for missiles traveling at hypersonic speeds is an inherently challenging task due to the fact that the system dynamics are nonlinear and highly coupled. An even more challenging task is the design of guidance laws for hypersonic missiles, which incorporates the terminal conditions to maximize target penetration. In this research, nonlinear control techniques are combined with optimal control methods to develop guidance and control laws for air-breathing hypersonic missiles in the presence of system uncertainty and external disturbances. One of the contributions of this research is detailed theoretical analysis of the performance characteristics of the proposed control design. Moreover, by including terminal constraints in the cost function, target penetration is maximized. Specifically, by minimizing angle of attack (AoA) and inertial angle of obliquity (AoO) at impact, maximum target penetration is achieved. Lyapunov-based stability analysis is utilized to prove the theoretical result, and high-fidelity numerical simulation results are provided to verify the practical performance of the proposed guidance law design.

Daisy Chaining Based Visual Servo Control Part II: Extensions, Applications and Open Problems

In this paper, the open problems and applications of a daisy chaining visual servo control strategy are given. This paper is Part II of Hu et al. (2007) in which a tracking problem using the daisy chaining strategy is addressed. The main idea of the daisy chaining strategy is to use multi-view geometry to relate coordinate frames attached to the moving camera, moving planar patch, and the desired planar patch specified by an a priori image. Geometric constructs developed for traditional camera-in-hand problems are fused with fixed-camera geometry to develop a set of Euclidean homographies. Based on the homographies, the corresponding rotation and translation components can be extracted for use in the control development. Different from the traditional camera-to-hand and camera-in-hand visual servo control configurations, two cameras are used to construct the homography relationships and estimate the pose of an object modeled as a planar patch (e.g., an unmanned ground vehicle (UGV) or an unmanned air vehicle (UAV)) when in case the object is out of the field of view (FOV), or when the current and desired poses of the object are within the FOV of a single camera.

Robust visual servo control in the presence of fruit motion for robotic citrus harvesting

Robust visual servo controller is formulated to compensate for unknown fruit motion.Lyapunov-based stability guarantees uniformly ultimately bounded regulation of the end-effector.By selecting appropriate control gains, the desired pick cycle time can be achieved.The developed controller is computationally inexpensive and easy to implement.The developed controller can easily be extended to non-citrus fruits with general ellipsoid geometry. Unknown fruit motion due to exogenous disturbances such as wind gust, canopy unloading, and particularly, fruit detachment forces can reduce overall harvesting efficiency in robotic fruit harvesting. Existing approaches relying on high-gain controllers to compensate for fruit motion are inherently susceptible to measurement noise and can lead to instability. The contribution of this paper is in the development of a robust image-based visual servo controller to regulate a robotic manipulator to a target fruit in the presence of unknown fruit motion. The robust feedback elements included in the control structure compensate for non-vanishing nonlinear disturbances without the need for high-gain feedback. Lyapunov-based stability analysis guarantees uniformly ultimately bounded regulation of the robot end-effector to a target fruit. In addition, finite-time convergence analysis is presented to show that the controller gains can be chosen to achieve the desired fruit removal rate, or cycle time. Numerical simulations with varying fruit displacement of { 35 , 70 , 105 , 140 , 175 , 210 } mm verify the feasibility of the developed controller while the performance is evaluated on a seven degrees-of-freedom kinematically redundant manipulator using an artificial citrus fruit moving with 120mm displacement. The developed robust controller demonstrates stable closed-loop operation of the system. Further, the effect of uncertainties in field conditions such as illumination variations and partial fruit occlusion, overlapping fruit, and obstacles on the developed controller are discussed.

Information fusion in human-robot collaboration using neural network representation

In this paper, an algorithm for hard and soft data fusion is developed for tracking moving objects using hard data from sensors on autonomous agents and soft data from human observations. Two main challenges are identified and addressed in this paper: 1. how to model the human observation, 2. how to estimate state using soft data and fuse it with the state estimates from the sensors on autonomous agents (e.g., a camera sensor). A novel approach is developed to model perceived human observations to the real physical states using artificial neural networks (ANN). A particle filter (PF) is used to estimate a moving targets state based on range and bearing observation data from a human observer and an EKF is used to estimate the target state using on-board camera sensor. The range measurement is represented using Kumaraswamys double bounded distribution. The state estimates computed based on a model of human observation learned by an ANN are fused with the state estimates from the on-board sensors using a fast covariance intersection (CI) algorithm. The CI algorithm yields consistent fused estimates in the absence of unknown correlations between state estimates obtained using human measurements and robot sensor measurements. The performance of the developed algorithms is validated on a target tracking simulation platform.

Immediately sequential bilateral cataract surgery: a cost-effective procedure

OBJECTIVE The objective of this project was to perform a cost-effectiveness analysis (CEA) of immediately sequential bilateral cataract surgery (ISBCS) versus delayed sequential bilateral cataract surgery (DSBCS) to determine whether ISBCS represents an appropriate, cost-effective way to rapidly rehabilitate a patients visual impairment. DESIGN A systematic review followed by a primary economic analysis with computer-based econometric modeling. PARTICIPANTS Not applicable. METHODS We constructed a decision analytic model from the perspective of the public third-party payer (i.e., the Ministry of Health) to conduct a CEA of both surgeries, ISBCS and DSBCS. Cost data consisted of the costs of the surgery, intravitreal injections, medications, and drops, all of which were obtained from a comprehensive literature search and from records at St. Josephs Hospital, London, Ont. The effectiveness was measured by the utility values associated with visual acuity in the better seeing eye. RESULTS ISBCS resulted in an incremental effectiveness of 0.08 utility at an incremental cost of

An Efficient Framework for Particle Filter-Based Urban Target Tracking

1607. Discounting the quality-adjusted life-years (QALYs) gained by an annual interest rate of 3% resulted in 0.932 QALYs gained. The cost-effectiveness of ISBCS was calculated to be

Stabilizing a nonlinear model-based networked control system with communication constraints

1431 per QALY gained. A 1-way sensitivity analysis was performed by varying costs, utility values, probabilities, and discounting rates. This analysis varied the incremental cost-effectiveness ratio but did not change the conclusion. CONCLUSIONS Health economics assessment showed that, compared with DSBCS, ISBCS is a cost-effective procedure. This finding will be highly useful to policy-makers, decision-makers, clinicians, hospital administrators, and payers in making cost-efficient decisions.