Robust tuning of PI controllers based on taguchi method

The internal model control (IMC) scheme has been widely applied in the field of process control. This is due to its simple and straightforward controller design procedure as well as its good disturbance rejection capabilities and robustness properties. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of the linear design procedures can be exploited directly. The resulting controllers are comparable to gain-scheduled PI or PID controllers which are the standard controllers in process industry. In practice, the tuning of conventional PI or PID controllers can be very time-consuming. In this paper, the design effort of the nonlinear IMC and conventional controller design methods are discussed and the control results are compared by applying it to a Hammerstein process and nonlinear temperature control of a heat exchanger.

ABSTRACTIn this article, we propose practical rules for tuning event-based PID controllers with two sampling strategies: symmetric send-on-delta (SSOD) and regular quantification (RQ). We present a detailed analysis about the effect of the derivative term of the controller when using SSOD or RQ and some guide lines are given to select the derivative filter coefficient. The two sampling strategies are compared, showing that, even when both of them lead to similar controlled output response, systems with RQ have better robustness properties than those with SSOD. The study is based on the describing function and the results are applicable to process with dynamic responses of different types: with time delays, non-minimum phase, under-damped response, etc. The rules presented here are given in terms of phase and gain margins that are measures of robustness used in the design of continuous PID controllers. This allows the application of conventional PID tuning methods to the case of event-based PID. The tuning rules are very simple and can be used for tuning PID, PI, PD and other controller structures.

A methodology of analysis of stability and performance of variable structure (VS) PID controllers is proposed. It is proposed that stability of the VS system can be approximately assessed via the describing function method. The describing function of the VS PID controller is derived. It is shown that the system with the VS PID controller is quasilinear. Tuning rules for VS PI controller for the level process are given. It is shown that the VS PI controller has higher performance than the conventional PI controller for the process considered.

AUTOMATIC TUNING OF PID CONTROLLERS FOR UNSTABLE PROCESSES

The PID controller is one of the control strategies having simple structure was understood by plant operator, which is comparatively easy to tune. Irrespective of the attractiveness, the tuning characteristic of PID controllers is a task for engineers and scientists. Numerous procedures for tuning PID-controllers are recommended such as: Ziegler Nichols tuning method auto-tuning, artificial intelligence and optimization techniques. Evolutionary optimization techniques (EOTs) as emergent techniques, an efficient GA-based tuning approach aiming at enhancing PID control for complex processes is proposed. The test and simulation results demonstrate that the GA-based proposed scheme is capable and can be used to overcome the flaws of traditional fixed gain controller. The main role of the article is to appraise the algorithm for tuning of PID-controller gains along with minimization of cost function and argue about the capability, computational efficiency of GA to solve the problems in control systems.

The paper presents a comparison of tuning procedures for a multi-region fuzzy-logic controller used for nonlinear process control. This controller is composed of local PID controllers and fuzzy-logic mechanism that aggregates local control signals. Three off-line tuning procedures are presented. The first one focuses on separate tuning of local PID controllers gains in the case when the parameters of membership functions of fuzzy-logic mechanism are know a priori. The second one consists of two major steps. In the first step, the local PID controllers gains are calibrated and in the next step the parameters of membership functions of fuzzy-logic mechanism are tuned. The third one focuses on tuning of PID controllers gains and membership functions parameters jointly. These procedures exploit evolutionary algorithm to optimize the performance of the multi-region fuzzy-logic controller with respect to integral quality criterion such as Integral Absolute Error (IAE). Effectiveness of these procedures is verified based on a well known from literature continuous nonlinear pH neutralization reactor model. For comparison a single PID controller tuned for specific work point is also taken into account.

Control problems have been experienced with FOPTD-based PID tuning methods when the normalized dead-time of the process model is small. One proposed solution has been to treat the FOPTD system model, with small normalized dead-time, as an integral plus dead time (IPDT) process for controller tuning purposes. There has been very little work though on investigating the transition point, as a normalized dead-time value, when the designer should choose to use either FOPTD-based or IPDT-based PID control design methods. This contribution considers finding the transition point(s) for the gain and phase margin design method. For a range of values of normalized dead-time the performance of the IPDT-based PI controller will be compared with that of the FOPTD-based PI controller for both set-point response and load disturbance rejection.

This paper presents the comparative study of Model Predictive Controller (MPC) with PID Controller implemented for flow control loop in LABVIEW. Classical PID controller is mostly used in process instrumentation yet tuning of PID controller is the crucial part to be dealt with. Moreover, changes in the parameters of the loop components affects the tuning of the controller, hence over a period of time retuning of PID controller is required. On the other hand MPC controller works on the process model which predicts and optimizes the process performance. Here, Flow control loop performance was studied for MPC and PID controller. MPC worked on 1st order state space system model and PID was tuned using Internal Model Control (IMC) method. The result of implementation shows that, MPC controller performs much better than PID controller. Set point of with less settling time and zero peak overshoot was observed in MPC whereas PID has comparatively more settling time and peak overshoot.

Networked Control Systems (NCS) are feedback/feed-forward control systems where control components (sensors, actuators and controllers) are distributed across a common communication network. In NCS, there exist network-induced random delays in each channel. This paper proposes a method to compensate the effects of these delays for the design and tuning of PID controllers. The control design is formulated as a constrained optimization problem and the controller stability and robustness criteria are incorporated as design constraints. The design is based on a polytopic description of the system using a Poisson pdf distribution of the delay. Simulation results are presented to demonstrate the performance of the proposed method.

In this article, an attempt is made to design the fractional order proportional integral derivative (FO-PID) controller for multi-input multi-output (MIMO) system using teaching learning-based optimization (TLBO) algorithm. The control of MIMO system always offers a complicated control paradigm due to its structure. It is realized that dynamic systems can be effectively represented by fractional order dynamics which is based on fractional calculus. The PID controller is still the most reliable controller due to its uncomplicated structure, and the FO-PID controller is predicted to have better performance as compared to conventional PID controller due to increment of two more parameters. The performance of non-integer PID controller depends upon the five constants; hence, the tuning of these five constants is most important for the optimum performance. In the presented work, TLBO technique is adopted for the tuning of the non-integer PID controller. The designed controller is implemented on the four tank MIMO system in MATLAB. The performance of the designed controller is also compared from the previous results available in literature.KeywordsFour tank MIMO systemFO-PIDTLBO

Temperature control is an important task in the modern industries like chemical, pharmaceutical etc. considering the real time load and set point variations in their plants. The present work gives experimental results for the control of a constant level stirred nonlinear temperature water bath with continuous inlet and outlet flow under load and set point disturbances. When the inlet temperature becomes comparable to the desired set point their effect becomes that much prominent in terms of rise time, overshoot and short term stability. In the present system, the level plays an important factor in the heat distribution. The slight variation in the inlet flow rate disturbs the system by varying its level assumed to be constant. Also the fluctuation in the mains voltage plays a major role in the short term stability of the system. In the work presented here, all these three disturbances have been considered using feedforward control. A real time PID controller taken with a fixed bias voltage has been used and tuned by Genetic Algorithm based Neuro Fuzzy Controller (GANFC) developed on the platform of Visual BASIC 6.0. The PID controller was designed and developed based on 89C51 microcontroller around MK-70 MLW bath with 10Litre capacity. A 1500W heater was operated between 0–240Vac with 256 different firing angles for the Triac. Fluke Hydra 2625A Data acquisition unit has been used for acquiring the data. The load disturbances have been created by varying inlet flow rate from 100m1/min to 500m1/min at 28°C for a set point of 40°C. Also the set point disturbance was induced by changing the set point from 35.0°C to 45.0°C. The constant level is maintained by keeping the outflow rate same as the inflow rate. Results for the modified GANFC, including feedforward control, were compared with GANFC controller without feedforward control.

This paper presents the time domain optimal tuning of fractional order Proportional Integral Derivative (FOPID) controller designed for higher order fractional model by using optimization with integral performance indices. The Integral Time Absolute Error (ITAE) is chosen as performance measure because it is one of the good tuning criterion to obtain controller parameters and widely used in PID controller design. The minimization of the ITAE is carried out by using Genetic Algorithm (GA). GA minimizes the ITAE and gives the optimum values of FOPID controller parameters. The purpose of this paper is to apply GA based ITAE minimization on higher order fractional delayed system to track the set point. Simulation results show the effectiveness of the proposed method.

In this paper, a new design scheme of performance-driven PID controllers whose PID parameters are adjusted based on a control performance criterion. Although a majority of studies have been focused on the derivation of the CPM index, the control parameter tuning method based on the CPM has been hardly studied. Conventional self-tuning controllers are tuned based on the variance of control errors and/or modeling errors. Few adaptive schemes use performance indice as tuning signals, which should be the main driving force in maintaining optimal operation, This paper develops a strategy for the tuning of an adaptive PID controller that is an approximation of a generalized minimum variance controller. The main driving signal for adaptive tuning is the degradation of the controller performance criterion. The effectiveness of the proposed method is numerically evaluated on two simulation examples.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

Real Time Control For Time Delay System