Human-in-the-Loop Model Predictive Control of an Irrigation Canal

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de Andalucía P11-TEP-812

Drag-free and attitude control for the GOCE satellite

The paper concerns Drag-Free and Attitude Control of the European satellite Gravity field and steady-state Ocean Circulation Explorer (GOCE) during the science phase. Design has followed Embedded Model Control, where a spacecraft/environment discrete-time model becomes the realtime control core and is interfaced to actuators and sensors via tuneable feedback laws. Drag-free control implies cancelling non-gravitational forces and all torques, leaving the satellite to free fall subject only to gravity. In addition, for reasons of science, the spacecraft must be carefully aligned to the local orbital frame, retrieved from range and rate of a Global Positioning System receiver. Accurate drag-free and attitude control requires proportional and low-noise thrusting, which in turn raises the problem of propellant saving. Six-axis drag-free control is driven by accurate acceleration measurements provided by the mission payload. Their angular components must be combined with the star-tracker attitude so as to compensate accelerometer drift. Simulated results are presented and discusse

Semi-batch reactor predictive control using MATLAB fmincon function compared to SOMA algorithm

In this paper the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area is studied. Two approaches to model predictive control applied on a nonlinear system are compared here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The comparison itself was made from four points of view. Firstly, the value of the in-reactor temperature overshoot and the related quality of the in-reactor temperature course were observed. Secondly, the time of processing which is important for effectiveness of a real plant and also the course of the actuating signal that is important from the practical point of view were monitored. The input data used here to simulate the process were obtained from the real chemical exothermic process. © 2018, World Scientific and Engineering Academy and Society. All rights reserved

Stochastic Model Predictive Control for Networked Systems with Random Delays and Packet Losses in All Channels

A stochastic Model Predictive Control strategy for control systems with communication networks between the sensor node and the controller and between the controller and the actuator node is proposed. Data packets are subject to random delays and packet loss is possible; acknowledgments for received packets are not provided. The expected value of a quadratic cost is minimized subject to linear constraints; the set of all initial states for which the resulting optimization problem is guaranteed to be feasible is provided. The state vector of the controlled plant is shown to converge to zero with probability one

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed. © 2018 The Authors, published by EDP Sciences

Robust Model Predictive Control with Anytime Estimation

With an increasing autonomy in modern control systems comes an increasing amount of sensor data to be processed, leading to overloaded computation and communication in the systems. For example, a vision-based robot controller processes large image data from cameras at high frequency to observe the robot’s state in the surrounding environment, which is used to compute control commands. In real-time control systems where large volume of data is processed for feedback control, the data-dependent state estimation can become a computation and communication bottleneck, resulting in potentially degraded control performance. Anytime algorithms, which offer a trade-off between execution time and accuracy of computation, can be leveraged in such systems. We present a Robust Model Predictive Control approach with an Anytime State Estimation Algorithm, which computes both the optimal control signal for the plant and the (time-varying) deadline/accuracy constraint for the anytime estimator. Our approach improves the system’s performance (concerning both the control performance and the estimation cost) over conventional controllers, which are designed for and operate at a fixed computation time/accuracy setting. We numerically evaluate our approach in an idealized motion model for navigation with both state and control constraints