Test-environment based on a team of miniature walking robots for evaluation of collaborative control methods

2012 American Control Conference (ACC)

Chlebek

Hanebeck

2012

The main problem of stochastic nonlinear model predictive control (SNMPC) is that the equations for state prediction and calculation of the expected reward are in general not solvable in closed form. A popular approach is to approximate the occurring continuous probability density functions by a discrete density representation, which allows an analytical solution of the SNMPC equations. In this paper, we propose to draw the samples not randomly as in Monte Carlo based methods, but systematically by minimizing a distance measure. In doing so, fewer components are generally required to represent the underlying probability density while achieving the same approximation quality. Especially if the evaluation of the expected reward is computationally expensive, this property affects the complexity of computation significantly. By means of a path planning problem, we have substantiated this statement with several simulation runs.

Section: A Simulation Setupmentioning

confidence: 99%

Open-loop feedback control of nonlinear stochastic systems based on deterministic dirac mixture densities

2012 American Control Conference (ACC)

Chlebek

Hanebeck

2012

“…To demonstrate the capabilities of the proposed conservative planning approach, we applied the LFM algorithm to a robot control problem. In the considered simulation setting, a robot [8] is supposed to reach a target area without colliding with obstacles. The pose of the robot can be estimated by means of the previous applied control inputs and distance measurements to two landmarks.…”

Section: Simulationsmentioning

confidence: 99%

Robust model predictive control with least favorable measurements

Lyons

2010 IEEE Conference on Multisensor Fusion and Integration

Kuderer

et al. 2010

Self Cite

Closed-loop model predictive control of nonlinear systems, whose internal states are not completely accessible, incorporates the impact of possible future measurements into the planning process. When planning ahead in time, those measurements are not known, so the closed-loop controller accounts for the expected impact of all potential measurements. We propose a novel conservative closed-loop control approach that does not calculate the expected impact of all measurements, but solely considers the single future measurement that has the worst impact on the control objective. In doing so, the model predictive controller guarantees robustness even in the face of high disturbances acting upon the system. Moreover, by considering only a single dedicated measurement, the complexity of closed-loop control is reduced significantly. The capabilities of our approach are evaluated by means of a path planning problem for a mobile robot.

“…1) moving on a 2D-plane with constant velocity. This omnidirectional robot is used in a specific mode of operation, where left and right turns are superimposed onto the forward motion [12]. Similar to the motion of twowheeled differential-drive vehicles, the motion can be modeled by means of a nonlinear discrete-time system equation…”

Section: Problem Formulationmentioning

confidence: 99%

“…The walking robots are part of a test environment built to evaluate control and localization methods [12]. For control purposes, an overhead camera overlooks a confined area, in which the robots move, and determines the true postions of the robots.…”

Section: A Description Of the Test Environmentmentioning

confidence: 99%

Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities

2010 IEEE International Conference on Control Applications

Lyons

Noack

et al. 2010

Self Cite

Abstract-In Model Predictive Control, the quality of control is highly dependent upon the model of the system under control. Therefore, a precise deterministic model is desirable. However, in real-world applications, modeling accuracy is typically limited and systems are generally affected by disturbances. Hence, it is important to systematically consider these uncertainties and to model them correctly. In this paper, we present a novel Nonlinear Model Predictive Control method for systems affected by two different types of perturbations that are modeled as being either stochastic or unknown but bounded quantities. We derive a formal generalization of the Nonlinear Model Predictive Control principle for considering both types of uncertainties simultaneously, which is achieved by using sets of probability densities. In doing so, a more robust and reliable control is obtained. The capabilities and benefits of our approach are demonstrated in real-world experiments with miniature walking robots.