Combining Multi-Level Real-time Iterations of Nonlinear Model Predictive Control to Realize Squatting Motions on Leo

Today‚Äôs humanoid robots are complex mechanical systems with many degrees of freedom that are built to achieve locomotion skills comparable to humans. In order to synthesize whole-body motions, real-tme capable direct methods of optimal control are a subject of contemporary research. To this end, Nonlinear Model Predictive Control is the method of choice to realize … Read more

An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization

We present a filter line-search algorithm for nonconvex continuous optimization that combines an augmented Lagrangian function and a constraint violation metric to accept and reject steps. The approach is motivated by real-time optimization applications that need to be executed on embedded computing platforms with limited memory and processor speeds. In particular, the proposed method enables … Read more

Approximating the solution for the multiparametric 0-1-mixed integer linear programming problem with interval data

In this paper we present algorithms to approximate the solution for the multiparametric 0-1-mixed integer linear programming problem relative to the objective function. We consider the uncertainty for the parameters that de fine the cost vector corresponding to a subset of 0-1-variables by assuming that each parameter belongs to a known interval. We suppose that we … Read more

Real-Time Optimization Strategies for Building Systems

We propose real-time optimization strategies for energy management in building systems. We have found that exploiting building-wide multivariable interactions between CO2 and humidity, pressure, occupancy, and temperature leads to significant reductions of energy intensity compared with traditional strategies. Our analysis indicates that it is possible to obtain energy savings of more than 50% compared with … Read more

A Fast Moving Horizon Estimation Algorithm Based on Nonlinear Programming Sensitivity

Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state estimation. Despite the attractiveness of this method, its application in industrial settings has been rather limited. This has been mainly due to the difficulty to solve, in real-time, the associated dynamic optimization problems. In this work, a fast MHE algorithm able to overcome this … Read more