8,414 research outputs found
Design of generalized minimum variance controllers for nonlinear multivariable systems
The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor
Neurogenesis and astrogenesis contribute to vestibular compensation in the neurectomized adult cat: cellular and behavioral evidence
Neurogenesis occurs in some regions of the adult mammalian brain and gives rise to neurons integrated into functional networks. In pathological or postlesional conditions, neurogenesis and astrogenesis can also occur, as demonstrated in the deafferented vestibular nuclei after unilateral vestibular neurectomy in the adult cat. Here we report that in cats infused with an antimitotic drug, cytosine-[beta]-D arabinofuranoside (AraC), the number of GAD67 and GFAP immunoreactive cells is increased, despite the total mitotic activity blockade observed in the deafferented vestibular nuclei after unilateral vestibular neurectomy. At the behavioral level, recovery of posturo-locomotor function was drastically delayed, and no alteration of the horizontal spontaneous nystagmus was observed. These cellular and behavioral results suggest that reactive neurogenesis and astrogenesis might contribute highly to vestibular compensation in the adult cat, probably by accelerating the recovery of vestibular functions
Improving Brain–Machine Interface Performance by Decoding Intended Future Movements
Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
Routing Games with Progressive Filling
Max-min fairness (MMF) is a widely known approach to a fair allocation of
bandwidth to each of the users in a network. This allocation can be computed by
uniformly raising the bandwidths of all users without violating capacity
constraints. We consider an extension of these allocations by raising the
bandwidth with arbitrary and not necessarily uniform time-depending velocities
(allocation rates). These allocations are used in a game-theoretic context for
routing choices, which we formalize in progressive filling games (PFGs).
We present a variety of results for equilibria in PFGs. We show that these
games possess pure Nash and strong equilibria. While computation in general is
NP-hard, there are polynomial-time algorithms for prominent classes of
Max-Min-Fair Games (MMFG), including the case when all users have the same
source-destination pair. We characterize prices of anarchy and stability for
pure Nash and strong equilibria in PFGs and MMFGs when players have different
or the same source-destination pairs. In addition, we show that when a designer
can adjust allocation rates, it is possible to design games with optimal strong
equilibria. Some initial results on polynomial-time algorithms in this
direction are also derived
An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
We describe a whole-body dynamic walking controller implemented as a convex
quadratic program. The controller solves an optimal control problem using an
approximate value function derived from a simple walking model while respecting
the dynamic, input, and contact constraints of the full robot dynamics. By
exploiting sparsity and temporal structure in the optimization with a custom
active-set algorithm, we surpass the performance of the best available
off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We
describe applications to balancing and walking tasks using the simulated Atlas
robot in the DARPA Virtual Robotics Challenge.Comment: 6 pages, published at ICRA 201
Active Queue Management for Fair Resource Allocation in Wireless Networks
This paper investigates the interaction between end-to-end flow control and MAC-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading, and a scheduler allocates the channel based on channel quality,but subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport layer flow control of TCP New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users
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