This paper provides a real time on-board algorithm for a biplane-quadrotor to iteratively learn a forward transition maneuver via repeated flight trials. The maneuver is controlled by regulating the pitch angle and propeller thrust according to feedforward control laws that are parameterized by polynomials. Based on a nominal model with simplified aerodynamics, the optimal coefficients of the polynomials are chosen through simulation such that the maneuver is completed with specified terminal conditions on altitude and air speed. In order to compensate for modeling errors, repeated flight trials are performed by updating the feedforward control parameters according to an iterative learning algorithm until the maneuver is perfected. A geometric attitude controller, valid for all flight modes is employed in order to track the pitch angle according to the feedforward law. Further, a high-fidelity thrust model of the propeller for varying advance-ratio and orientation angle is obtained from wind tunnel data which is captured using a neural network model. This facilitates accurate application of feedforward thrust for varying flow conditions during transition. Experimental flight trials are performed to demonstrate the robustness and rapid convergence of the proposed learning algorithm.

The wheeled inverted pendulum (WIP) is an underactuated, nonholonomic mechatronic system, and has been popularized commercially as the Segway. Designing a control law for motion planning, that incorporates the state and control constraints, while respecting the configuration manifold, is a challenging problem. In this article, we derive a discrete-time model of the WIP system using discrete mechanics and generate optimal trajectories for the WIP system by solving a discrete-time constrained optimal control problem. Furthermore, we describe a nonlinear continuous-time model with parameters for designing a closed-loop linear-quadratic regulator (LQR). A dual control architecture is implemented in which the designed optimal trajectory is, then, provided as a reference to the robot with the optimal control trajectory as a feedforward control action, and an LQR in the feedback mode is employed to mitigate noise and disturbances for ensuing stable motion of the WIP system. While performing experiments on the WIP system involving aggressive maneuvers with fairly sharp turns, we found a high degree of congruence in the designed optimal trajectories and the path traced by the robot while tracking these trajectories. This corroborates the validity of the nonlinear model and the control scheme. Finally, these experiments demonstrate the highly nonlinear nature of the WIP system and robustness of the control scheme.

Locomotion at low Reynolds numbers is a topic of growing interest, spurred by its various engineering and medical applications. This paper presents a novel prototype and a locomotion algorithm for the 3-link planar Purcell's swimmer based on Lie algebraic notions. The kinematic model, based on Cox theory of the prototype swimmer is a driftless control-affine system. Using the existing strong controllability and related results, the existence of motion primitives is initially shown. The Lie algebra of the control vector fields is then used to synthesize control profiles to generate motions along the basis of the Lie algebra associated with the structure group of the system. An open loop control system with vision-based positioning is successfully implemented which allows tracking any given continuous trajectory of the position and orientation of the swimmer's base link. Alongside, the paper also provides a theoretical interpretation of the symmetry arguments presented in the existing literature to generate the control profiles of the swimmer.

Often, data used in online decision making (for example, in determining how to react to changes in process behavior, traffic flow control, etc.) is dynamic in nature and hence the timeliness of the data delivered to the decision making process becomes very important. The delivered data must conform to certain time or value based application specific consistency requirements. The design of mechanisms for such data delivery is challenging given that dynamic data changes rapidly and unpredictably, the latter making it very hard to use simple prediction techniques. To address these challenges we develop mechanisms to obtain timely and consistency-preserving updates for dynamic data by pulling data from the source at strategically chosen points in time, providing quality of service (QoS) guarantees. Motivated by the need for practical system design, but using formal analytical techniques, we offer a systematic approach based on control-theoretic principles. We present a stochastic controller based on the linear quadratic Gaussian (LQG) technique as a means for deciding when to next refresh data from a source. A simple enhancement of the LQG algorithm allows us to provide QoS guarantees. Using real-world traces of real-time data we show the superior performance of our feedback-driven control-theoretic approach by comparing with a previously proposed adaptive refresh technique, a pattern matching technique, and a proportional controller with dynamically changing tuning criteria.

The RLS or its equivalent H2algorithms achieve the best average performance. But these suffer from poor worst case performance. Stochastic gradient based algorithms (like LMS) achieve best worst case performance, but a poor average performance. In this paper we propose switching criteria for acquiring advantages of both of these algorithms. The proposed algorithm uses a nonlinear combination of H2optimal and H∞optimal estimation. We present a mixed H2/H∞algorithm employing an exponential window and the achievable bound for this estimation strategy.

In this paper, an extended risk-sensitive filter (ERSF) is used to estimate the motion parameters of an object recursively from a sequence of monocular images. The effect of varying the risk factor /spl theta/ on the estimation error is examined. The performance of the filter is compared with the extended Kalman filter (EKF) and the theoretical Cramer-Rao lower bound. When the risk factor /spl theta/ and the uncertainty in the measurement noise are large, the initial estimation error of the ERSF is less than that of the corresponding EKF The ERSF is also found to converge to the steady state value of the error faster than the EKF. In situations when the uncertainty in the initial estimate is large and the EKF diverges, the ERSF converges with small errors. In confirmation with the theory, as /spl theta/ tends to zero, the behavior of the ERSF is the same as that of the EKF.