Discrete PID Research Articles

A Design of Nonlinear PID Controller with Neural net based FRIT

Some design schemes of model-free controllers which do not require any system models have been considered in the last decade. FRIT(Fictitious Reference Iterative Tuning) method that directly computes the control parameters from the operating data have been proposed as the one of model-free controllers. FRIT has some useful practical features. One is that it does not require system identification. Another is that the control parameters can be directly computed using only a set of closed loop input/output data and the desired output signal. The calculations of the control parameters needs the optimization of the cost functions. The ordinary approach is the gradient method. However, this calculations derives only linear parameters. Therefore, the applications of FRIT are limited for linear systems. In this paper, a new approach to the discrete FRIT-based nonlinear PID control is proposed. The neural network is utilized for the optimization of FRIT. PID parameters are adequately adjusted corresponding to the nonlinear properties. The conventional schemes by using the neural networks require the information of system Jacobian to update weighting factors. This proposed method can calculate the control parameters without the information of system Jacobian or system parameters except for the information about the time-delay.

Research on Overrunning-clutch Speed Coupling Control Strategy in a Parallel Plug-in Hybrid Electric Vehicle

To resolve issues of powertrain components’ speed coupling based on overrunning-clutch which is applied to the Parallel Plug-in Hybrid Electric Vehicle (PHEV), the speed coupling control strategy of a PHEV is put forward. The controller models are established by using Matlab/Simulink according to the structure of overrunning-clutch, and the discrete PID controllers accurately control powertrain components’ output speed. The real-time simulation is performed on dSPACE platform. Experimental results indicate that powertrain components’ output speed are coupled accurately and the power are distributed, which achieve the smooth power switching requirements and satisfy load capacity.

MPSO based Model Order Formulation Scheme for Discrete PID Controller Design

MPSO based Model Order Formulation Scheme for Discrete PID Controller Design

Grey fuzzy PI control for packing pressure during injection molding process

This study presents a grey fuzzy PI (GFPI) controller for packing pressure control during the injection process. The novel controller was designed for solving the problems of large overshoot, static error, and long delay time on servo motor-driven injection molding machines. Based on reasonable assumptions, a mathematical model of injection system was established. According to the nonlinear, severe interferences, and long delay time characteristics in injection process, this paper integrates predictive grey system, robust fuzzy ratiocination, and PI control. Using these mathematical model and control algorithms, a GFPI controller is implemented into an MCU using C programming techniques. The integral discrete PID controller and solid fuzzy controller were realized for contrast experiments. The experimental results show that GFPI controller had better performance on reducing overshoot and static error, increasing both response rate and repeatable accuracy. Such a developed technology would provide helpful references for designing the controller of energy-saving servo motor-driven injection molding equipment.

A Design of a FRIT Based Nonlinear PID Controller

Some design schemes of model-free controllers which do not require any system models have been considered in the last decade. FRIT(Fictitious Reference Iterative Tuning) method that directly computes the control parameters from the operating data have been proposed as the one of model-free controllers. FRIT has some useful practical features. One is that it does not require system identification. Another is that the control parameters can be directly computed using only a set of closed loop input/output data and the desired output signal. The calculations of the control parameters needs the optimization of the cost functions. The ordinary approach is the gradient method. However, this calculations derives only linear parameters. Therefore, the applications of FRIT are limited for linear systems. In this paper, a new approach to the discrete FRIT-based nonlinear PID control is proposed. The neural network is utilized for the optimization of FRIT. PID parameters are adequately adjusted corresponding to the nonlinear properties. The conventional schemes by using the neural networks require the information of system Jacobian to update weighting factors. This proposed method can calculate the control parameters without the information of system Jacobian or system parameters except for the information about the time-delay.

Design of discrete time adaptive PID control systems with parallel feedforward compensator

This paper deals with the design of an adaptive PID control system with a parallel feedforward compensator (PFC) for discrete-time SISO systems and its application to water level control of a 3-tank system. The proposed method utilizes the characteristics of almost strict positive realness (ASPR) of the controlled plant. A conventional design scheme of a PFC which realizes an ASPR augmented controlled plant is also proposed. Further it is shown that the introduction of an internal model improves the control performance of the control system with the PFC. The effectiveness of the proposed method is confirmed through water level control experiments on a three-tank SISO system.

Performance of Fractional PID Algorithms Controlling Nonlinear Systems with Saturation and Backlash Phenomena

The development of fractional-order controllers is currently one of the most promising fields of research. However, most of the work in this area addresses the case of linear systems. This paper reports on the analysis of fractional-order control of nonlinear systems. The performance of discrete fractional-order PID controllers in the presence of several nonlinearities is discussed. Some results are provided that indicate the superior robustness of such algorithms.

CLOSED LOOP TUNING OF DISCRETE PID REGULATORS USING GENETIC ALGORITHMS

In the present paper, an experimental method based on genetic algorithms for the closed loop parameter tuning of discrete PID regulators, is presented. Starting with the closed loop step response, the parameters of an approximated second order discrete model are identified in a first stage and after, the PID parameters which result in a significant response improvement, are determined. In both stages, a procedure based on genetic algorithms is used to find the optimal solutions. A simulated example of the proposed method is presented in this paper.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Performance Improvement of Discrete Time PID Controller using Neural Networks

Performance Improvement of Discrete Time PID Controller using Neural Networks

On-line optimization in designing discrete IMC-based PID controllers

PID control schemes have been widely used in most process control systems for a long time. However, it is a very important problem how to choose a suitable set of PID parameters. Because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for time-varying systems. In this paper, a new self-tuning PID control scheme is proposed for such systems, which is based on the discrete-time IMC tuning method. Then, some user-specified parameters included in the newly proposed scheme can be adjusted by using Nelder-Mead method in an on-line manner.

Feedback Regulation of Temperature in FCC Regenerator Reactors

Design of regulative control laws for stabilization of regenerator temperature in adiabatic Fluid Catalytic Cracking Units is performed. This is achieved despite the fact that kinetic terms and heat generation are, commonly, unknown. This article presents a control law that makes use of on-line discrete measurements of temperature to estimate the heat of reaction, obtained from a neural-estimation algorithm. The controller design is similar to discrete PID controllers. Some new tuning rules are given in terms of closed loop and measured characteristic times. In order to obtain the simplest neural network and the minimum number of parameters to be adjusted, only one perceptron is used. Moreover, the resulting control law has physical meaning and can be interpreted as a standard PID controller with time varying gain. Performance of the neural-estimation technique is analyzed under closed-loop behavior using numerical simulations considering disturbances to the process and set point changes. The performance obtained when using this new control is compared with the one obtained by using a PID controller tuned following the IMC rules.

Modeling and control of the magnetic suspension system

Abstract A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

A design scheme of discrete robust PID control systems and its application

PID controllers have been widely used in many chemical processes. Because they have only three control parameters, we can easily understand their physical meanings. However, it is difficult to tune their parameters in the case where the control system has uncertainties, for example, modeling errors, system fluctuation, and so on. Therefore, we propose a design scheme of discrete robust PID controllers in this paper. That is, we design a PID controller in connection with the generalized minimum variance control (GMVC) scheme, and present a scheme on how to design a weighting factor included in the GMVC criterion from the viewpoint of robust stability. Furthermore, we investigate the behavior of the proposed control scheme by applying it to an air pressure control system. © 1999 Scripta Technica, Electr Eng Jpn, 128(4): 77–83, 1999

A design scheme of discrete robust PID control systems and its application

PID controllers have been widely used in many chemical processes. Because they have only three control parameters, we can easily understand their physical meanings. However, it is difficult to tune their parameters in the case where the control system has uncertainties, for example, modeling errors, system fluctuation, and so on. Therefore, we propose a design scheme of discrete robust PID controllers in this paper. That is, we design a PID controller in connection with the generalized minimum variance control (GMVC) scheme, and present a scheme on how to design a weighting factor included in the GMVC criterion from the viewpoint of robust stability. Furthermore, we investigate the behavior of the proposed control scheme by applying it to an air pressure control system. © 1999 Scripta Technica, Electr Eng Jpn, 128(4): 77–83, 1999

Dynamic simulation and control of direct rotary dryers

Adaptive and conventional control of a direct rotary dryer has been assessed through simulations. A dynamic model of the process was developed specifically for this purpose. The model is based on mass and energy balances and includes a drying kinetic derived from mass transfer principles. In the modelling process, ten discrete perfectly mixed elements were considered for the dryer with each containing four differential equations. A well tuned discrete PID and an adaptive algorithm were used to control the outlet solid humidity. Both controllers can regulate the controlled variable for different disturbances, although the adaptive controller is more effective.

離散時間ロバストPID制御系の設計法とその応用

PID controllers have been widely used in many chemical processes. Because they have only three control parameters, we can easily understand their physical meanings. However, it is difficult to tune their parameters in the case where the control system has uncertainties, for example, modeling errors, system fluctuation, and so on. Therefore, we propose a design scheme of discrete robust PID controllers in this paper. That is, we design a PID controller in connection with the generalized minimum variance control (GMVC) scheme, and present a scheme on how to design a weighting factor included in the GMVC criterion from the viewpoint of robust stability. Furthermore, we investigate the behavior of the proposed control scheme by applying it to an air pressure control system. © 1999 Scripta Technica, Electr Eng Jpn, 128(4): 77–83, 1999

Flux and torque control of switched reluctance machines

The theory and implementation of a flux-linkage controller applied to a switched reluctance machine drive are described. Analysis of the simplified discrete time load model shows that a simple flux-linkage controller can produce a ‘dead-beat’ system response. Comparison of the response from a discrete time PID current controller, hysteresis current controller and the new flux-linkage controller, show that the flux controller has a much improved response. The experimental controller is used to control an IGBT converter, driving a commercial 7.5 kW switched reluctance motor. A method of constant torque operation is introduced. This takes account of limitations imposed by the finite DC-link voltage and rotor speed when calculating the reference values. The method is based on ‘flux ramps’, where the ramps are chosen to give constant shaft torque, and remain within the capabilities of the power converter. Correct selection of the flux ramps allows constant torque operation to be achieved over a wide speed and torque range. Measured results for the experimental drive demonstrate the low torque ripple achieved for motoring and generating operation over a range of speed and torque values.

Implementation Issues for Discrete PID Algorithms Using Shift and Delta Operators Parameterizations

PID controllers have been the most popular controllers in process and industrial control applications for over 50 years and continue to maintain popularity despite opportunities to apply more exotic methodologies. This is because of their simple structure, robustness and successful commercial performance. Although the tuning concepts of PID controllers dominate the studies in this are, few studies address the parameterization issues and the Implementational concepts of such controllers. In this paper a comparative study of the coefficient finite word length effects for the canonical realizations of different discrete PID algorithms using delta (δ) and shift (z) operators is described. The problem of coefficient quantization for different PID algorithms for fixed-word length for the two realizations is discussed. A generic PID hardware architecture is proposed to perform the controller structures for both operators.

Disk Drive Actuator BIAS: Prediction and Compensation

Disk drive actuator bias, which is the average torque (current) required to maintain a set actuator position, has a profound impact on the performance of a disk drive by influencing the settle time at the hand-off from seek mode to track follow mode. With an adequate prediction of bias, the settling time of a seek can be reduced by properly selecting the initial conditions of the track follow controller at the hand-off. In this paper, a state-space prediction algorithm is derived which is based Dahl's friction model and which accounts for much of the hysteresis behavior of bias. Based on an accurate bias prediction, a method is also developed for choosing the optimal initial condition of the integrator of a discrete time PiD controller which minimizes the seek settle time.