Enhancing Control Systems Response Using Genetic PID Controllers

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-PID controller for industrial motor drives. Both the design of fuzzy-PID controller and its integration with the conventional PID in global control system to produce a hybrid design is demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the fuzzy-PID controller. The objective is to utilize the best attributes of the PID and fuzzy-PID controllers to provide a controller, which will produce better response than either the PID or the fuzzy-PID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background, a fuzzy-PID controller, which, is ready to take over the PID controller when severe disturbs occur. The hybrid controller is formulated and implemented in real-time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital signal processor (DSP)-based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Experimental results show that the proposed fuzzy- PID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

Although the traditional PID controller is widely used in many fields, the system parameters varying and external disturbances existing in the DC servo system will cause large overshoot or poor stability. To improve the performance of the PID controller, a compound servo control system combining the conventional PID control and the fuzzy control is presented to meet the demand of a vehicular antenna servo system in this paper. Incorporating the fuzzy control and the conventional PID control, this paper presents a design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas. Simulation results are presented to show the efficiency of the proposed controller. The practical control effect shows that the control system that adopts the fuzzy PID controller has better performance than that of the traditional PID control system, and meets the performance requirements of the servo system.

The traditional manual operation mode has problems such as high manpower cost, low efficiency, low safety, low operating precision, etc. Achieving unmanned intelligent systems in the field of mechanical and electrical engineering has become the key to transforming and upgrading relevant enterprises and improving their overall competitiveness. In this paper, an improved fuzzy PID control system is constructed based on a PLC control system combined with a fuzzy PID intelligence algorithm. It is applied to the pumping motor, coagulation dosing system, and other specific devices for simulation experiments, compared to the conventional PID algorithm, and evaluated the control system in this paper. The fuzzy PID control algorithm of this paper reduces the steady-state adjustment time of the response curve by 30% compared to the conventional PID algorithm, and the overshooting amount is reduced by 25%. With almost no overshoot, the pumping motor that was equipped with this paper’s PID algorithm reached its stable target speed in 0.3 seconds. In addition, this study found that in the coagulation dosing system model incorporating the fuzzy PID algorithm of this paper, when the model is accurate, only 20% of the overshooting amount occurs after the system has been running for some time. The conventional PID algorithm’s 50% overshoot is significantly less than this, and the time it takes to reach a steady state is also 50 minutes earlier than the conventional PID control. When the model is imprecise, fluctuations occur in both conventional PID control and fuzzy PID control, but the amplitude of the fluctuations in fuzzy PID control is relatively small. In conclusion, this paper shows that the fuzzy PID control algorithm in this paper has higher control accuracy, response speed, and stability than the conventional PID control algorithm.

In this paper existing sliding mode control for nonlinear dynamic systems is demonstrated and its performance is evaluated against conventional PID controller . The changes in system dynamics occurs due to parametric uncertainties and external disturbances. Here it is shown that the sliding mode control based on the variable structure control is a robust control technique against parametric uncertainties and external disturbances. The effect of chattering in SMC is reduced using a boundary layer approach. The PID controller being a fixed structure control system is sensitive to parametric uncertainties and external disturbances. The sliding mode control and PID controller are illustrated in computer simulation for modeled spring-mass dashpot system and coupled tank system. Finally the performances of SMC are compared with conventional PID controller for the above systems to show the dominance of SMC over conventional PID controller.

During the past two decades, a new paradigm in control engineering, called networked control system (NCS), has emerged. This has been shown to be a successful alternative to traditional control systems. The present study designs a reinforcement learning controller based on the concept of Dynamic Fuzzy Q-Learning (DFQL). This controls the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) of a wind energy conversion system (WECS) through a communication network. A comparative performance investigation has been carried out with two other controllers; namely a conventional PID and Q-Learning controller. The performance of these controllers is studied considering various imperfections of the communication network such as random delay and packet dropout. Simulations are carried out considering a WECS consisting of 20-DFIGs of 5MW capacity each. The results of the study demonstrate that the DFQL controller shows better performance compared to Q-Learning and conventional PID controllers.

Present paper attempts to review the literature related to design of P-I-D control for time delayed complex industrial process for single as well as for multivariable process with interaction considerations, their decoupler design and time delay compensators. General instrumentation of the industrial feedback control systems along with control system analysis has been covered. Also it covers, control features of some paper mill sub-processes like headbox operation, basis weight and retention. The importance of eliminating the effects of interactions, among the process control loops inside a multi input multi output industrial control system, has been discussed with the help of literature study. The importance of the process dynamics knowledge for designing a control system has also been investigated. This paper also investigates the significance and effectiveness of PID controllers through various literature studies. The problems of the classical PID controllers such as constraints, presence of disturbances etc.) can be removed by designing in combination with soft computing techniques. Moreover, possibility of further enhancements in the PID controller with the utilization of various schemes available, has been presented. The present status of control systems in industrial processes in terms of various control parameters such as stability, dead time compensation etc. has been presented and the future improvements have been stated.

In the field of satellite technology and its application, most of the satellite attitude control system uses PID control. However, the conventional PID control has certain limitations in large inertial system, nonlinear system and complex system. In order to overcome these problems, this paper puts forward a novel fuzzy PID controller by using the radial basis function neural network, genetic algorithm and Takagi-Sugeno fuzzy logic. Based on the dynamic model of the satellite attitude, the approach uses the rule from the improved fuzzy PID controller to ensure system stability. And the proposed control system is found to own many excellent performance of faster response speed, higher efficiency and smaller overshoot errors compared with the conventional PID control system. Finally, simulation results and comparative analysis are carried out to demonstrate that the proposed controller applied in the satellite attitude control system can effectively achieve attitude control and improve the response sensitivity. Therefore, it is feasible and effective in practical application to meet the demands of the Euler angles for satellite attitude control.

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

In this paper, a restricted structure of PID controller is proposed for automobile gas turbine by considering a LQG cost function. So PID Controller coefficients are calculated by minimizing this cost function. One of the most important characteristics of this approach, its ability to design various structure PID controllers by solving optimization problem only for one time, moreover it can approximate main optimization problem by a repetitive Quadratic optimization Problem (QP). This approach is convex optimization and has an analytic solution; therefore its calculations are not complicated. In defined cost function, there are two weighting functions, control error and control action; they are adjusted to meet economical (control action) and operational (control error) requirement. Moreover, using restricted structure control criterions prepare appropriate conditions for designer. Restricted structure controller based on LQG, has proper low frequency gain and limited output variance. At first, appropriate input and output for gas turbine system is chosen and pre compensator is designed for decoupling of loops. After that mentioned controller is designed and applied. Simulation results indicate desired performance of gas turbine closed loop system with designed controller.

SummaryProportional Integral Derivative (PID) controllers still represent the core control method for achieving output regulation of either linear or nonlinear systems in the majority of industrial applications. However, conventional PID control cannot guarantee specific state constraint requirements for the plant, when the system introduces uncertainties. In this paper, a novel nonlinear PID control that achieves output regulation and guarantees a desired state limitation below a given value for a wide class of nonlinear systems with constant uncertainties is proposed. Using nonlinear ultimate boundedness theory, it is shown that the proposed state‐limiting PID (sl‐PID) control maintains a given bound for the desired system states at all times, ie, even during transients, whereas an analytic method for selecting the controller gains is also presented to ensure closed‐loop system stability and convergence at the desired equilibrium. Two nonlinear engineering examples that include an electric motor and a dc/dc converter are investigated using the conventional PID and the proposed sl‐PID to validate the superiority of the proposed controller in achieving the desired output regulation with a given bounded state requirement.

Cascade fuzzy control of a tubular chemical reactor

A fractional order PID control strategy in active magnetic bearing systems

This paper presents an implementation of conventional PID (CPID) controller using Ziegler-Nichols rules and fuzzy PD (FPD) controller for position servo motor control based on Lab View (Laboratory Virtual Instrument Engineering Workbench Environment) through Data Acquisition (DAQ) Device PCI- 6521 of National Instrument's and Data Acquisition Accessory Board Model (CB-68LP).CPID controller is perhaps the most well-known and most widely used in industrial applications. However, it has been known that CPID controller generally don’t work well for non-linear systems, higher order and time-delayed linear system and particularly complex and vague system. To overcome these difficulties, this paper proposes to use the FPD controller for a servo motor system instead of CPID. The parameters of servo motor used are completely unknown. The FPD structure has two-input single-output and fairly similar characteristic to its conventional counterpart and provides good performance. Simple rules base are used for FPD (nine rules only). Performance evaluation was carried out via a comparison study for the proposed control scheme and other existing control scheme, such as CPID controller. The critical point for this experiment on position system is a steady state error and settling time. The performance showing that the FPD has less settling time and zero steady state error over its CPID. The algorithms of FPD and CPID controllers are implemented using PID, Fuzzy Logic and simulation toolkits of the Lab View environment.

The aerothermic process is a pilot scale heating and ventilation system. It is equipped with a heater grid and a centrifugal blower, fully connected through the Humusoft MF624 data acquisition system for real time control. The interaction between its main variables is considered as challenging for mono-variable controllers. An abrupt change in the ventilator speed might cause an undesirable disturbance in the air temperature representing a factor that must be managed to conserve energy. To annul the effect of this interaction, this paper presents an experimental comparison between three forms of the PID controller: the conventional PID controller, the PI-D controller and its decentralized version. A multi-variable continuous state space model is obtained from on-line experimental data. The outcome of the experimental results is that the main control objectives, such as set-point tracking and interactions rejection, are well achieved for the temperature and the air flow simultaneously.