Controlled Synchronization of Coupled Pendulums by Koopman Model Predictive Control

We propose and experimentally demonstrate a feedback control method that allows synchronizing the motion of a chain of several coupled nonlinear oscillators actuated through one end of the chain. The chain considered in this work is a one-dimensional array of pendulums pivoting around a single axis and interacting with adjacent pendulums through torsion springs; the array is actuated using a single torque motor attached to one of the two boundary pendulums. This represents a mechanical realization of the Frenkel-Kontorova model { a spatially discrete version of a sine-Gordon equation describing (nonlinear) waves. The main challenges of controlling these systems are: high order (the number of pendulums can be high), nonlinear dynamics, and (as we set the problem here) only one actuator. The presented problem of synchronization of motion is a special case of the problem of reference tracking, where all pendulums reach a common point or a trajectory. In particular, we demonstrate synchronization to a stable equilibrium (all pendulums downward), unstable equilibrium (all pendulums upward), and a periodic orbit (all pendulums revolving). We use the Koopman Model Predictive Control (KMPC) that constructs a linear predictor of the nonlinear system in a higher-dimensional lifted space and uses the predictor within a classical linear MPC, thereby maintaining low computational cost that allows for a real-time implementation, while taking into account the complex nonlinear dynamics

Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot

Many robotic applications, such as milling, gluing, or high precision measurements, require the exact following of a pre-defined geometric path. In this paper, we investigate the real-time feasible implementation of model predictive path-following control for an industrial robot. We consider constrained output path following with and without reference speed assignment. We present results from an implementation of the proposed model predictive path-following controller on a KUKA LWR IV robot.Comment: 8 pages, 3 figures; final revised versio

Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization