Model predictive control of a CSTR: A comparative study among linear and nonlinear model approaches

© 2017 IEEE. This paper presents a comparative study of two widely accepted model predictive control schemes based on mixed logical dynamical (MLD) and nonlinear modeling approaches with application to a continuous stirred tank reactor (CSTR) system. Specifically, we approximate the nonlinear behavior of a CSTR system with multiple local linear models in a MLD framework. The main benefit of such a scheme is the significant improvement in model accuracy when compared with a single linearized model. The benefits and trade-offs associated with predictive control laws synthesized using MLD and nonlinear modeling approaches are also compared.National Research Foundation, Singapore

Nonlinear model predictive control based on Bernstein global optimization with application to a nonlinear CSTR

© 2016 EUCA. We present a model predictive control based tracking problem for nonlinear systems based on global optimization. Specifically, we introduce a 'Bernstein global optimization' procedure and demonstrate its applicability to the aforementioned control problem. This Bernstein global optimization procedure is applied to predictive control of a nonlinear CSTR system. Its strength and benefits are compared with those of a sub-optimal procedure, as implemented in MATLAB using fmincon function, and two well established global optimization procedures, BARON and BMIBNB.National Research Foundation, Singapore

PyGOM - A Python Package for Simplifying Modelling with Systems of Ordinary Differential Equations

Ordinary Differential Equations (ODE) are used throughout science where the capture of rates of change in states is sought. While both pieces of commercial and open software exist to study such systems, their efficient and accurate usage frequently requires deep understanding of mathematics and programming. The package we present here, PyGOM, seeks to remove these obstacles for models based on ODE systems. We provide a simple interface for the construction of such systems backed by a comprehensive and easy to use tool--box. This tool--box implements functions to easily perform common operations for ODE systems such as solving, parameter estimation, and stochastic simulation. The package source is freely available and organized in a way that permits easy extension. With both the algebraic and numeric calculations performed automatically (but still accessible), the end user is freed to focus on model development.Comment: 23 pages, 6 figure

Optimal nonlinear model predictive control based on Bernstein polynomial approach

© 2017 IEEE. In this paper, we compare the performance of Bernstein global optimization algorithm based nonlinear model predictive control (NMPC) with a power system stabilizer and linear model predictive control (MPC) for the excitation control of a single machine infinite bus power system. The control simulation studies with Bernstein algorithm based NMPC show improvement in the system damping and settling time when compared with respect to a power system stabilizer and linear MPC scheme. Further, the efficacy of the Bernstein algorithm is also compared with global optimization solver BMIBNB from YALMIP toolbox in terms of NMPC scheme and results are found to be satisfactory.National Research Foundation, Singapore

Optimal Operation Strategy for Biohydrogen Production

Hydrogen produced by microalgae is intensively researched as a potential alternative to conventional energy sources. Scaling-up of the process is still an open issue, and to this end, accurate dynamic modeling is very important. A challenge in the development of these highly nonlinear dynamic models is the estimation of the associated kinetic parameters. This work presents the estimation of the parameters of a revised Droop model for biohydrogen production by Cyanothece sp. ATCC 51142 in batch and fed-batch reactors. The latter reactor type results in an optimal control problem in which the influent concentration of nitrate is optimized which has never been considered previously. The kinetic model developed is demonstrated to predict experimental data to a high degree of accuracy. A key contribution of this work is the prediction that hydrogen productivity can achieve 3365 mL/L through an optimally controlled fed-batch process, corresponding to an increase of 116% over other recently published strategies.Author E. A. del Rio-Chanona would like to acknowledge CONACyT scholarship No. 522530 and the Secretariat of Public Education and the Mexican government for funding this project. Author P. Dechatiwongse is supported by a scholarship from the Royal Thai Government, Thailand. Solar Hydrogen Project was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), project reference EP/F00270X/1.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.iecr.5b0061

The four-tank benchmark: a simple solution by embedded model control

The four-tank benchmark is a multivariate and nonlinear control problem which has been widely studied in the literature. Two pairs of tanks in series are supplied by two pumps. Under certain configurations, the Embedded Model Control approach provides a simple decoupled solution by separately controlling the two output tank levels and treating the input flow as a partly unknown disturbance. Neglected dynamics in a form of unknown delays both in sensors and actuator dynamics is considered. The core of the control unit is a discrete-time embedded model consisting of unknown disturbance dynamics and partly known nonlinear interactions. The embedded model is driven by the plant command and by a feedback vector which is retrieved from the model error. The feedback is capable of keeping updated the unknown disturbance prediction, ready to be cancelled by the control law. The control gains are tuned using two sets of closed-loop eigenvalues in order to trade-off between disturbance rejection and robust stability. Simulated runs under different tank interactions prove design effectiveness

Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack

Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars, where safety and lives are at stake. Recently, the robustness of existing skeleton-based HAR methods has been questioned due to their vulnerability to adversarial attacks, which causes concerns considering the scale of the implication. However, the proposed attacks require the full-knowledge of the attacked classifier, which is overly restrictive. In this paper, we show such threats indeed exist, even when the attacker only has access to the input/output of the model. To this end, we propose the very first black-box adversarial attack approach in skeleton-based HAR called BASAR. BASAR explores the interplay between the classification boundary and the natural motion manifold. To our best knowledge, this is the first time data manifold is introduced in adversarial attacks on time series. Via BASAR, we find on-manifold adversarial samples are extremely deceitful and rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold. Through exhaustive evaluation, we show that BASAR can deliver successful attacks across classifiers, datasets, and attack modes. By attack, BASAR helps identify the potential causes of the model vulnerability and provides insights on possible improvements. Finally, to mitigate the newly identified threat, we propose a new adversarial training approach by leveraging the sophisticated distributions of on/off-manifold adversarial samples, called mixed manifold-based adversarial training (MMAT). MMAT can successfully help defend against adversarial attacks without compromising classification accuracy.Comment: arXiv admin note: substantial text overlap with arXiv:2103.0526