Tsonyo Slavov

Fed-batch cultivation control based on genetic algorithm PID controller tuning

In this paper a universal discrete PID controller for the control of E. coli fed-batch cultivation processes is designed. The controller is used to control feed rate and to maintain glucose concentration at the desired set point. Tuning the PID controller, to achieve good closed-loop system performance, using genetic algorithms is proposed. As a result the optimal PID controller settings are obtained. For a short time the controller sets the control variable and maintains it at the desired set point during the process. Application of the designed controller provides maintaining of the accuracy and efficiency of the system performance.

PID Controller Tuning based on Metaheuristic Algorithms for Bioprocess Control

ABSTRACT This paper presents an optimal tuning of a universal digital PID controller using metaheuristics as Genetic Algorithms (GA), Simulated Annealing (SA) and Tabu Search (TS). The controllers were used to control the feed rate and to maintain the glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The mathematical model of the cultivation process was represented by the dynamic mass balance equations for biomass and substrate. In the control algorithm the design measurement and process noise as well as the time delay of the glucose measurement system were taken into account. To achieve good closed-loop system performance metaheuristics based controller tuning was done. By tuning the constants (Kp Ti, Td b, c and N) in the PID controller algorithm, the controller can provide control action designed for the specific process requirements. To evaluate the significance of the tuning procedure and controller performance different criteria were used. Objective function values and CPU time were used as criteria to compare the performance of the three metaheuristic algorithms—GA, SA and TS. A series of procedures for PID controller tuning were performed using competing techniques and criteria. As a result the set of optimal PID controller settings was obtained. For a short time the controller set the control variable and maintained it at the desired set point during the E. coli MC4110 fed-batch cultivation process. The simulation results indicate that the proposed metaheuristic algorithms are effective and efficient, and demonstrate that the applied techniques exhibit a significant performance improvement over classical optimization methods.

Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model

Abstract An algorithm for multiple model adaptive control of a time-variant plant in the presence of measurement noise is proposed. This algorithm controls the plant using a bank of PID controllers designed on the base of time invariant input/output models. The control signal is formed as weighting sum of the control signals of local PID controllers. The main contribution of the paper is the objective function minimized to determine the weighting coefficients. The proposed algorithm minimizes the sum of the square general error between the model bank output and the plant output. An equation for on-line determination of the weighting coefficients is obtained. They are determined by the current value of the general error covariance matrix. The main advantage of the algorithm is that the derived general error covariance matrix equation is the same as this in the recursive least square algorithm. Thus, most of the well known RLS modifications for the tracking timevariant parameters can be directly implemented. The algorithm performance is tested by simulation. Results with both SISO and MIMO time variant plants are obtained.

A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control

In this paper a hybrid scheme using Firefly Algorithm (FA) - Genetic Algorithm (GA) is introduced. The novel hybrid meta-heuristics algorithm is realized and applied to PID controller parameter tuning in Smith Predictor for a nonlinear control system. The controller is used to control feed rate and to maintain glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The hybrid FA-GA adjustments are done based on several pre-tests. Simulation results indicate that the applied hybrid algorithm is effective. Good closed-loop system performance is achieved on the basis of the considered PID controllers tuning procedures. Moreover, the observed results are compared to the ones obtained by applying the pure FA and pure GA. The comparison shows that the proposed hybrid algorithm is highly competitive with standard FA and GA for considered here optimization problem.

Design of Embedded Robust Control Systems Using MATLAB® / Simulink®

Robust control theory allows for changes in a system whilst maintaining stability and performance. Applications of this technique are very important for dependable embedded systems, making technologies such as drones and other autonomous systems with sophisticated embedded controllers and systems relatively common-place. The aim of this book is to present the theoretical and practical aspects of embedded robust control design and implementation with the aid of MATLAB® and SIMULINK®. It covers methods suitable for practical implementations, combining knowledge from control system design and computer engineering to describe the entire design cycle. Three extended case studies are developed in depth: embedded control of a tank physical model; robust control of a miniature helicopter; and robust control of two-wheeled robots. These are taken from the area of motion control but the book may be also used by designers in other areas. Some knowledge of Linear Control Theory is assumed and knowledge of C programming is desirable but to make the book accessible to engineers new to the field and to students, the authors avoid complicated mathematical proofs and overwhelming computer architecture technical details. All programs used in the examples and case studies are freely downloadable to help with the assimilation of the book contents.

Identification and synthesis of linear-quadratic regulator for digital control of electrohydraulic steering system

The main objective of this work is to present the designed system for control of electrohydraulic steering system that is implemented in low speed mobile machines. The goal of control algorithm is to achieve fast transient response without overshooting and static error in whole working range. To achieve this aim first a multivariable dynamical plant model is estimated by identification procedure. The model obtained is validated by various statistical tests. The multivariable LQR regulator with integral action and Kalman filter are designed. Appropriate software which is implemented in 32-bit microcontroller is developed. Experimental results are presented which confirm that the control system achieves the prescribed performance.