77,162 research outputs found
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
In this work, we consider the problem of decentralized multi-robot target
tracking and obstacle avoidance in dynamic environments. Each robot executes a
local motion planning algorithm which is based on model predictive control
(MPC). The planner is designed as a quadratic program, subject to constraints
on robot dynamics and obstacle avoidance. Repulsive potential field functions
are employed to avoid obstacles. The novelty of our approach lies in embedding
these non-linear potential field functions as constraints within a convex
optimization framework. Our method convexifies non-convex constraints and
dependencies, by replacing them as pre-computed external input forces in robot
dynamics. The proposed algorithm additionally incorporates different methods to
avoid field local minima problems associated with using potential field
functions in planning. The motion planner does not enforce predefined
trajectories or any formation geometry on the robots and is a comprehensive
solution for cooperative obstacle avoidance in the context of multi-robot
target tracking. We perform simulation studies in different environmental
scenarios to showcase the convergence and efficacy of the proposed algorithm.
Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M
Optimal tracking for pairs of qubit states
In classical control theory, tracking refers to the ability to perform
measurements and feedback on a classical system in order to enforce some
desired dynamics. In this paper we investigate a simple version of quantum
tracking, namely, we look at how to optimally transform the state of a single
qubit into a given target state, when the system can be prepared in two
different ways, and the target state depends on the choice of preparation. We
propose a tracking strategy that is proved to be optimal for any input and
target states. Applications in the context of state discrimination, state
purification, state stabilization and state-dependent quantum cloning are
presented, where existing optimality results are recovered and extended.Comment: 15 pages, 8 figures. Extensive revision of text, optimality results
extended, other physical applications include
Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions
An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work
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