Control design of uncertain quantum systems with fuzzy estimators

published_or_final_versio

Automatic Planning and Control of Robot Natural Motion Via Feedback

A feedback control strategy for the command of robot motion includes some limited automatic planning capabilities. These may be seen as sequential solution algorithms implemented by the robot arm interpreted as a mechanical analog computer. This perspective lends additional insight into the manner in which such control techniques may fail, and motivates a fresh look at requisite sensory capabilities. For more information: Kod*La

Reducing Adverse Self-Medication Behaviors in Older Adults with Hypertension: Results of an e-health Clinical Efficacy Trial

A randomized controlled efficacy trial targeting older adults with hypertension (age 60 and over) provided an e-health, tailored intervention with the “next generation” of the Personal Education Program (PEP-NG). Eleven primary care practices with advanced practice registered nurse (APRN) providers participated. Participants (N = 160) were randomly assigned by the PEP-NG (accessed via a wireless touchscreen tablet computer) to either control (entailing data collection and four routine APRN visits) or tailored intervention (involving PEP-NG intervention and four focused APRN visits) group. Compared to patients in the control group, patients receiving the PEP-NG e-health intervention achieved significant increases in both self-medication knowledge and self-efficacy measures, with large effect sizes. Among patients not at BP targets upon entry to the study, therapy intensification in controls (increased antihypertensive dose and/or an additional antihypertensive) was significant (p = .001) with an odds ratio of 21.27 in the control compared to the intervention group. Among patients not at BP targets on visit 1, there was a significant declining linear trend in proportion of the intervention group taking NSAIDs 21–31 days/month (p = 0.008). Satisfaction with the PEP-NG and the APRN provider relationship was high in both groups. These results suggest that the PEP-NG e-health intervention in primary care practices is effective in increasing knowledge and self-efficacy, as well as improving behavior regarding adverse self-medication practices among older adults with hypertension

New Results For Identifiability of Nonlinear-Systems

WOS: A1987F76720000

Rapid incoherent control of quantum systems based on continuous measurements and reference model

A rapid incoherent control scheme is proposed for driving a quantum system from an initial mixed state to a final state having a high fidelity with a target pure state. This scheme consists of two main steps: rapid purification of the initial mixed state and time-optimal control of the conditional state. The first step rapidly purifies the initial mixed state into an almost-pure state (conditional state) through continuous measurements and feedback control. The second step finds a set of suitable time-optimal controls through the control design of a reference model to drive the quantum system from the conditional state to the final state. The switching time between the two steps is determined by the expected purity or fidelity and the resultant state is an almost-pure state having a high fidelity with the target pure state. As an illustrative example, the authors apply the present rapid incoherent control scheme to a spin system. The potential applications of model predictive control to quantum systems are also discussed briefly. © The Institution of Engineering and Technology 2009.link_to_subscribed_fulltex

Fidelity-based probabilistic Q-learning for control of quantum systems

The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin-1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process. © 2012 IEEE

Admittance Model Haptic Interaction for Large Workspace Teleoperation

4nonenoneF. Antoniazzi; P. Gallina; A. Gasparetto; G. RosatiF., Antoniazzi; Gallina, Paolo; A., Gasparetto; G., Rosat