Search

In this paper, we introduce a set-theoretic approach for mobile robot localization with infrastructure-based sensing. The proposed method computes sets that over-bound the robot body and orientation under an assumption of known noise bounds on the sensor and robot motion model. We establish theoretical properties and computational approaches for this set-theoretic localization approach and illustrate its application to an automated valet parking example in simulations and to omnidirectional robot localization problems in real-world experiments. We demonstrate that the set-theoretic localization method can perform robustly against uncertainty set initialization and sensor noises compared to the FastSLAM.

Summary An iterative model and trajectory refinement (IMTR) strategy is proposed for trajectory optimization of nonlinear systems. A high- and a low-fidelity models are used. The high-fidelity model accurately represents the system but is not easily amenable to trajectory optimization, because of degree of nonlinearity, computational cost, or to being of “black-box” type. The low-fidelity model is suitable for numerical optimization but approximates the system dynamics with an error. The IMTR method is proposed to systematically iterate between the 2 models and efficiently converge on a control solution. Examples are drawn from orbital mechanics. The IMTR approach is compared to optimal nonlinear quadratic control using Pontryagin maximum principle. A convergence criterion for the IMTR iterations is established.

With the number of small Unmanned Aircraft Systems (sUAS) in the national airspace projected to increase in the next few years, there is growing interest in a traffic management system capable of handling the demands of this aviation sector. It is expected that such a system will involve trajectory prediction, uncertainty propagation, and path planning algorithms. In this work, we use linear covariance propagation in combination with a quadratic programming-based collision detection algorithm to rapidly validate declared flight plans. Additionally, these algorithms are combined with a Dynamic, Informed RRT* algorithm, resulting in a computationally efficient algorithm for chance-constrained path planning. Detailed numerical examples for both fixed-wing and quadrotor sUAS models are presented., Comment: Submitted to Advanced Control for Applications

SUMMARY In this paper, two nonlinear model predictive control (MPC) strategies are applied to solve a low thrust interplanetary rendezvous problem. Each employs a unique, nonclassical parameterization of the control to adapt the nonlinear MPC approach to interplanetary orbital dynamics with low control authority. The approach is demonstrated numerically for a minimum-fuel Earth-to-Mars rendezvous maneuver, cast as a simplified coplanar circular orbit heliocentric transfer problem. The interplanetary transfer is accomplished by repeated solution of an optimal control problem over (i) a receding horizon with fixed number of control subintervals and (ii) a receding horizon with shrinking number of control subintervals, with a doubling strategy to maintain controllability. In both cases, the end time is left unconstrained. The performances of the nonlinear MPC strategies in terms of computation time, fuel consumption, and transfer time are compared for a constant thrust nuclear-electric propulsion system. For this example, the ability to withstand unmodeled effects and control allocation errors is verified. The second strategy, with shrinking number of control subintervals, is also shown to easily handle the more complicated bounded thrust nuclear-electric case, as well as a state-control-constrained solar-electric case. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Traditionally, rendezvous and proximity maneuvers have been performed using open-loop maneuver planning techniques and ad hoc error corrections. In this paper, a Model Predictive Control (MPC) approach is applied to spacecraft rendezvous and proximity maneuvering problems in the orbital plane. We demonstrate that various constraints arising in these maneuvers can be effectively handled with the MPC approach. These include constraints on thrust magnitude, constraints on spacecraft positioning within Line-of-Sight cone while approaching the docking port on a target platform, and constraints on approach velocity to match the velocity of the docking port. The two cases of a nonrotating and a rotating (tumbling) platform are treated separately, and trajectories are evaluated in terms of maneuver time and fuel consumption. For the case when the platform is not rotating and the docking port position is fixed with respect to the chosen frame, an explicit offline solution of the MPC optimization problem is shown to be possible; this explicit solution has a form of a piecewise affine control law suitable for online implementation without an on-board optimizer. In the case of a fast rotating platform, it is, however, shown that the prediction of the platform rotation is necessary to successfully accomplish the maneuvers and to reduce fuel consumption. Finally, the proposed approach is applied to debris avoidance maneuvers with the debris in the spacecraft rendezvous path. The significance of this paper is in demonstrating that Model Predictive Control can be an effective feedback control approach to satisfy various maneuver requirements, reduce fuel consumption, and provide robustness to disturbances. Copyright © 2012 John Wiley & Sons, Ltd.

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.

With the increase in automotive powertrain complexity, an upfront assessment of powertrain capability in meeting its design targets is important early on in the development programs. The optimization of control policy based on powertrain simulation models can facilitate this assessment and establish limits of achievable performance for a given powertrain configuration and parameters. The paper discusses several computational optimization and user interface solutions for deploying a numerical optimal control approach in a user-friendly software environment.

SUMMARY The paper addresses an on-line, simultaneous input and parameter estimation problem for a first-order system affected by measurement noise. This problem is motivated by practical applications in the area of engine control. Our approach combines an input observer for the unknown input with a set-membership algorithm to estimate the parameter. The set-membership algorithm takes advantage of a priori available information such as (i) known bounds on the unknown input, measurement noise and time rate of change of the unknown input; (ii) the form of the input observer in which the unknown parameter affects only the observer output; and (iii) the input observer error bounds for the case when the parameter is known exactly. The asymptotic properties of the algorithm as the observer gain increases are delineated. It is shown that for accurate estimation the unknown input needs to approach the known bounds a sufficient number of times (these time instants need not be known). Powertrain control applications are discussed and a simulation example based on application to engine control is reported. A generalization of the basic ideas to higher order systems is also elaborated. Copyright # 2006 John Wiley & Sons, Ltd.

SUMMARY A hybrid feedback control law is proposed for the RTAC system. This hybrid feedback control law is expressed in terms of a continuous feedback part and a part that includes switched parameters determined according to a logic-based switching rule. By appropriate selection of the switching rule, previous theoretical results guarantee that the origin is globally asymptotically stable. Some comments are made about the closed-loop properties, and experiments confirm that good responses are obtained for the case studied. ( 1998 John Wiley & Sons, Ltd.

Discrete-time, linear control systems with specified pointwise-in-time constraints, such as those imposed by actuator saturation, are considered. The constraints are enforced by the addition of a nonlinear ‘reference governor’ that attenuates, when necessary, the input commands. Because the constraints are satisfied, the control system remains linear and undesirable response effects such as instability due to saturation are avoided. The nonlinear action of the reference governor is defined in terms of a finitely determined maximal output admissible set and can be implemented on-line for systems of moderately high order. The main result is global in nature: if the input command converges to a statically admissible input and the initial state of the system belongs to the maximal output admissible set, the eventual action of the reference governor is a unit delay.