PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “optimal control”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources
PubMed1997

Analysis of an optimal control model of multi-joint arm movements.

In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and Y show an "N' shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement. When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements.

Arm↗
PubMed1996

Minimum-time running and swimming: an optimal control approach.

During analysis of the competitor's velocity in a run, strong assumptions are imposed upon the runner's tactic. It is assumed that the competitor uses his/her maximal propulsive force in short-distance events. The runner's velocity is assumed constant in long-distance races. None of these assumptions is satisfied during middle-distance races. In this study, the competitor's velocity, minimizing the time taken to cover the distance, is determined by means of extremization of linear integrals using Green's theorem (Miele's method). The model of the competitor's motion is based on two differential equations: the first one derives from Newton's second law, the second one is the equation for power balance. The theory is illustrated with two examples referring to competitive running and swimming. The minimum-time competitive run can be broken into three phases: -the acceleration, -the cruise with the constant velocity, and -the negative kick at the end of the race. The problem has a similar solution in competitive swimming, however, the acceleration is replaced by the gliding phase.

Acceleration↗
PubMed2004

Expansion and contraction of the cytotoxic T lymphocyte response--an optimal control approach.

The kinetics of the cytotoxic T lymphocyte (CTL) response against intracellular pathogens has been found to have many stereotypical features that appear to be programmed early in the infection. We explain these findings here in terms of CTL response kinetics that minimize the probability that a pathological symptom will occur in association with the infection and its eradication. We assume that both the infection and the CTLs contribute to this pathology. We find that contraction kinetics is influenced by the relative pathogenicities of infection and CTLs, as well as on the virulence of the infection and the efficiency of the CTLs, but not by the magnitude of expansion or the dose and duration of infection. Our analysis explains the finding that the duration of the CTL expansion is highly stereotypical, with the maximum expansion of the CTL response dependent on the dose of the infection. Finally, we show that the stereotypical nature of CTL kinetics relies upon stringent regulation of the rates at which CTLs proliferate and die.

Communicable Diseases↗
PubMed2001

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Automation↗
PubMed2005

Approximate optimal control as a model for motor learning.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein's (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm.

Brain↗
PubMed2006

Quantum optimal control: Hessian analysis of the control landscape.

Seeking an effective quantum control entails searching over a landscape defined as the objective as a functional of the control field. This paper considers the problem of driving a state-to-state transition in a finite level quantum system, and analyzes the local topology of the landscape of the final transition probability in terms of the variables specifying the control field. Numerical calculation of the eigenvalues of the Hessian of the transition probability with respect to the control field variables reveals systematic structure in the spectra reflecting the existence of a generic and simple control landscape topology. An illustration shows that the number of nonzero Hessian eigenvalues is determined by the number of quantum states in the system. The Hessian eigenvectors associated with its nonzero eigenvalues are shown to give insight into the cooperative roles of the control variables. The practical consequences of these findings for quantum control are discussed.

Journal Article↗