Anti windup implementation on different PID structures

Although there have been tremendous advances in control theory over the last 25 years, the PID controller remains very popular and is still widely used in industry. A vital aspect of its implementation is the selection of a suitable set of parameters, as an improperly tuned controller might lead to adverse effects on process operation and worse, cause system instability. In industry, there are various types of PID controllers in addition to the 'textbook' PID but most tuning methods were developed based on this ideal algorithm. Another issue that is always associated with PID controllers is integral windup and the most popular method to overcome this problem is to add an anti windup compensator. This article includes the assessment of three anti windup strategies in combination with different tuning methods. The characteristics of PID controllers tuned using these approaches are evaluated by application to simulated FOPTD processes with different time-delay to time-constant ratios. Different measures were used to assess their performance and robustness properties, and the applicability of the tuning relationships to more typical (non-ideal) PID controllers is also considered. In general, the anti windup compensators successfully reduced the degradation effect caused by integral windup. It was found that the effectiveness of the different anti windup schemes varied depending on controller tuning methods and controller structures

PID-based control of a single-link flexible manipulator in vertical motion with genetic optimisation

This paper presents an investigation into dynamic simulation and controller optimization based on genetic algorithms (GAs) for a single-link flexible manipulator system in vertical plane motion. The dynamic model of the system is derived using the Lagrange equation and discretised using the finite difference (FD) method. GA optimization is used to optimize the parameters of the proportional-integral-derivative (PID) based controllers for control of rigid-body and flexible motion dynamics of the system. The important point is to evaluate the range of PID parameter which used in the GAs programmed to find the best value of this parameter. Comparative performance assessment of the control approaches are presented and discussed in the time and the frequency domains

On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Performance and Robustness Metrics for the Compensation of Processes with Time Delay

Proportional-integral (PI) or proportional-integral-derivative (PID) controllers are still by far the most common form of automatic feedback control employed in the process industries at present. It is estimated that more than 90% of control loops in the process control industry are of the PID type. Unfortunately, it is a common occurrence, in the majority of industries where this type of control is employed, for the performance of PI/PID controlled processes to be poor. There are a number of factors influencing this poor performance, namely controller tuning problems, controller equipment design restrictions, deficiencies in control strategy design, etc. The research was conducted with the intention of developing an affective strategy for assessing the performance and robustness of closed loop systems controlled by PI and PID controllers. Subsequent to an initial investigation into the most effective performance assessment measures, a Matlab based software tool to automate the evaluation process was developed. This software tool incorporated an identification stage followed by an evaluation stage and was developed using Matlab TM 6.5. A comparison of two distinctive identification techniques was conducted with the intention of identifying the most efficient means of extracting the most constructive information with minimal disturbance to the system under test. The identification techniques investigated included a relay based techniques and a Pseudorandom binary sequence testing scheme. A thorough investigation was carried out into identifying the most efficient means of applying these testing schemes. With respect to the Pseudorandom binary sequence based identification technique, a comparison of a number of technique parameter selection methods was carried out and a guideline for applying a pseudorandom binary test signal was developed. Also, a PI(D) tuning rule database was created. The effectiveness of the overall evaluation strategy, as well as the capabilities of the identification techniques was investigated in order to validate the merit of the strategy developed

Swarm-intelligence tuned current reduction for power assisted steering control in electric vehicle

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle needs to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm intelligence technique namely Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PIDParticle Swarm Optimization and PID-Ant Colony Optimization are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method

Design and Control of EMS Magnetic Levitation Train using Fuzzy MRAS and PID Controllers

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnetbased totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A Matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller

Complex order control for improved loop-shaping in precision positioning

This paper presents a complex order filter developed and subsequently integrated into a PID-based controller design. The nonlinear filter is designed with reset elements to have describing function based frequency response similar to that of a linear (practically non-implementable) complex order filter. This allows for a design which has a negative gain slope and a corresponding positive phase slope as desired from a loop-shaping controller-design perspective. This approach enables improvement in precision tracking without compromising the bandwidth or stability requirements. The proposed designs are tested on a planar precision positioning stage and performance compared with PID and other state-of-the-art reset based controllers to showcase the advantages of this filter

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested

Depth of anesthesia control using internal model control techniques

The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These discrepancies are translated into the pharmacokinetics (PK), and pharmacodynamics (PD). These uncertainties may affect the stability of the closed loop control system. This paper aims at developing predictive controllers using Internal Model Control technique. This study develops patient dose-response models and to provide an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated compared with traditional PID controller and the performance is confirmed in our simulation