Flexible Motion Optimization with Modulated Assistive Forces

Animated motions should be simple to direct while also being plausible. We present a flexible keyframe-based character animation system that generates plausible simulated motions for both physically-feasible and physically-infeasible motion specifications. We introduce a novel control parameterization, optimizing over internal actions, external assistive-force modulation, and keyframe timing. Our method allows for emergent behaviors between keyframes, does not require advance knowledge of contacts or exact motion timing, supports the creation of physically impossible motions, and allows for near-interactive motion creation. The use of a shooting method allows for the use of any black-box simulator. We present results for a variety of 2D and 3D characters and motions, using sparse and dense keyframes. We compare our control parameterization scheme against other possible approaches for incorporating external assistive forces.

Optimal control of fish-feeding in a three-dimensional calm freshwater pond considering environmental concern

<p style='text-indent:20px;'>This paper describes the optimal fish-feeding in a three-dimensional calm freshwater pond based on the concentrations of seven water quality variables. A certain number of baby fishes are inserted into the pond simultaneously. They are then taken out of the pond simultaneously for harvest after having gone through a feeding program. This feeding program creates additional loads of water quality variables in the pond, which becomes pollutants. Thus, an optimal fish-feeding problem is formulated to maximize the final weight of the fishes, subject to the restrictions that the fishes are not under-fed and over-fed and the concentrations of the pollutants created by the fish-feeding program are not too large. A computational scheme using the finite element Galerkin scheme for the three-dimensional cubic domain and the control parameterization method is developed for solving the problem. Finally, a numerical example is solved.</p>

Numerical Methods for Solving Optimal Control Problem Using Scaling Boubaker Function

In this paper, the approximation method was used for solving optimal control problem (OCP), two techniques for state parameterization and control parameterization have been considered with the aid of Scaling Polynomials (SBP) represent a new important technique for solving (OCP’s). The algorithms were illustrated by several numerical examples using Matlab program. The results were evaluated and graphed to show the accuracy of the methods.

Adaptive control parameterization method by density functions for optimal control problems

This paper proposes an efficient adaptive control parameterization method for solving optimal control problems. In this method, mesh density functions are used to generate mesh points. In the first step, the problem is solved by control parameterization on uniform mesh points. Then at each step, the approximate control obtained from the previous step is applied to construct a mesh density function, and consequently a new adapted set of mesh points. Several numerical examples are included to demonstrate that the adaptive control parameterization method is more accurate than a uniform control parameterization one.

A Multistage Feedback Control Strategy for Producing 1,3-Propanediol in Microbial Continuous Fermentation

In this paper, we consider a multistage feedback control strategy for producing 1,3-propanediol in microbial continuous fermentation. Both the dilution rate and the concentration of glycerol in the input feed are used as control variables, and these variables are further assumed to be in the form of a linear combination of biomass and glycerol concentrations. Unlike the general form of linear feedback control, the coefficients of linear combination are continuous functions with respect to time. Inspired by the control parameterization method, we use the piecewise-constant functions to approximate the coefficient functions; then we get the multistage feedback control law by solving nonlinear mathematical programming problems. Numerical results indicate the flexibility and effectiveness of our strategy.