scispace - formally typeset
Search or ask a question

Showing papers on "PID controller published in 1995"



Journal ArticleDOI
TL;DR: The results in this paper can be used to predict the achievable rise time of the closed-loop system, which is useful for self-diagnosis—a desirable feature of ‘intelligent’ controllers.

543 citations


Book
01 Jan 1995
TL;DR: In this paper, the Proportional-Integral-Derivative (PID) algorithm is used to tune a PID controller for dynamic performance. But, it is not suitable for non-linear processes.
Abstract: 1 Introduction to Process Control 2 Control Objectives and Benefits 3 Mathematical Modeling Principles 4 Modeling and Analysis for Process Control 5 Dynamic Behavior of Typical Process Systems 6 Empirical Model Identification 7 The Feedback Loop 8 The Proportional-Integral-Derivative (PID) Algorithm 9 PID Controller Tuning for Dynamic Performance 10 Stability Analysis and Controller Tuning 11 Digital Implementation of Process Control 12 Practical Application of Feedback Control 13 Performance of Feedback Control Systems 14 Cascade Control 15 Feedforward Control 16 Adapting Single-Loop Control Systems for Non-Linear Processes 17 Inferential Control 18 Level and Inventory Control 19 Single-Variable Model Predictive Control 20 Multiloop ControlEffects of Interaction 21 Multiloop Control Performance Analysis 22 Variable Structure and Constraint Control 23 Centralized Multivariable Control 24 Process Control Design Definition and Decisions 25 Process Control Design Managing the Design Procedure 26 Control for Product Quality and Profit Appendices

453 citations


Journal ArticleDOI
TL;DR: A software package developed in SIMULINK is discussed that enables the behavior of feedback control systems with actuator saturation and PID controllers to be evaluated and the performance of four different anti-windup implementations for PI or PID controllers is compared.
Abstract: This article describes software developed in SIMULINK that enables the behavior of feedback control systems with actuator saturation and PID controllers to be evaluated. The software, which is part command- and part menu-driven, allows a choice of four PID controllers using different integral wind-up strategies and transfer function entry of the actuator and plant dynamics. Most realistic control systems contain nonlinearities of some form. One nonlinearity commonly found in control systems is a saturating element. If integral control is applied to such a system to eliminate steady state error, an undesired side effect known as integrator windup may occur when large setpoint changes are made. This effect leads to a characteristic step response with a large overshoot and a very high settling time. To avoid this situation, many different anti-windup strategies have been suggested. This article discusses a software package that has been developed in the SIMULINK/MATLAB environment to investigate and compare the performance of four different anti-windup implementations for PI or PID controllers. The software is partially menu-driven and enables the user to easily enter his own actuator and plant transfer functions to study the performance with the different controllers. >

316 citations


Journal ArticleDOI
TL;DR: The control loop performance monitor (CLPM) as discussed by the authors detects oscillations in the control loop, which are normally caused by too-high friction in the controller valve, but there are other reasons as well.

285 citations


Journal ArticleDOI
TL;DR: Approximate analytical formulas to compute gain and phase margins of PID control systems are derived in this paper to facilitate online computation which would be particularly useful for implementing adaptive control.
Abstract: The performance and robustness of well-known PID formulas for process with deadtime to time constant ratio between 0.1 and 1 are discussed in this paper. The Ziegler-Nichols, Cohen-Coon, and tuning formulas that optimize for load disturbance response (integral absolute error, integral squared error, and integral time-weighted absolute error) give gain margins of about 1.5. The phase margins increase from about 30 to 60/spl deg/ as the process deadtime to time constant ratio increases from 0.1 to 1. Tuning formulas that optimize setpoint response give gain margins of about two and phase margins of about 65/spl deg/. These formulas mostly make use of the proportional-integral derivative (PID) controller zeros to cancel the process poles. Approximate analytical formulas to compute gain and phase margins of PID control systems are also derived in this paper to facilitate online computation which would be particularly useful for implementing adaptive control.

272 citations


Journal ArticleDOI
TL;DR: The proposed tuning rules are inspired from the symmetrical optimum principles and have the advantage to take into account both robustness aspects and desired closed-loop characteristics and to cover a large domain of current, real applications.

219 citations


Journal ArticleDOI
01 Mar 1995-Robotica
TL;DR: This paper proposes some simple rules for PID tuning of robot manipulators by using a suitable Lyapunov function together with the LaSalle invariance principle, and shows that with this guideline, the overall closed-loop system is asymptotically stable.
Abstract: In this paper we propose some simple rules for PID tuning of robot manipulators. The procedure suggested requires the knowledge of the structure of the inertia matrix and the gravitational torque vector of the robot dynamics, but only upper bounds on the dynamics parameters are needed. This tuning procedure is extracted from the stability analysis by using a suitable Lyapunov function together with the LaSalle invariance principle. We show that with this guideline, the overall closed-loop system is asymptotically stable. This procedure is illustrated for a two degrees-of-freedom robot

155 citations


Journal ArticleDOI
18 Jun 1995
TL;DR: This paper presents a fuzzy logic design approach that can meet the speed tracking requirements even when detuning occurs and computer simulations and experimental results obtained via a general-purpose digital signal processor (DSP) system are presented.
Abstract: Field orientation control (FOC) of induction machines has permitted fast transient response by decoupled torque and flux control. However, field orientation detuning caused by parameter variations is a major difficulty for indirect FOC methods. Traditional probability density function (PID) controllers have trouble meeting a wide range of speed tracking performance even when proper field orientation is achieved. PID controller performance is severely degraded when detuning occurs. This paper presents a fuzzy logic design approach that can meet the speed tracking requirements even when detuning occurs. Computer simulations and experimental results obtained via a general-purpose digital signal processor (DSP) system are presented.

154 citations


Journal ArticleDOI
TL;DR: A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed to reduce the computational requirement for identifying a fuzzy model.
Abstract: Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The /spl delta/ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by /spl delta/ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive. >

153 citations


Journal ArticleDOI
01 Jul 1995
TL;DR: In this article, a dynamic recurrent neural network (DRNN) is proposed to identify and control a class of control affine systems, which can be used in the context of differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.
Abstract: A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Journal ArticleDOI
TL;DR: A new algorithm for automatic tuning of decentralized PID control for two-input two-output (TITO) plants that fully extends the single-loop relay auto-tuner to the multiloop case is presented and seems to be efficient and robust.

Journal ArticleDOI
TL;DR: In this article, a nonlinear proportional and derivative (NPD) controller is proposed for both non-contact transient force control (NCTFC) and contact transient forces control (CTFC).
Abstract: This article describes a nonlinear proportional and derivative (NPD) controller and its use for both non-contact transient force control (NCTFC) (zero velocity when contact occurs) and contact transient force control (CTFC) (non-zero velocity when contact occurs). The key advantages of NPD control are its high disturbance rejection and robustness to time delay. The authors present a gain design method for NCTFC. Simulations and experiments on the Sarcos Dextrous Arm show that PD control becomes unstable for CTFC while NPD control is stable and reaches the steady state quickly. Experiments with human subjects on NCTFC and CTFC are also presented. >


Patent
01 Nov 1995
TL;DR: In this paper, a neural network is used as an inner-loop controller in a process control system having a constraint management scheme which prevents integral windup by controlling the action of the outer-loop controllers when limiting is detected in the associated manipulated-variable control path.
Abstract: A method and apparatus for a robust process control system that utilizes a neural-network multivariable inner-loop PD controller cascaded with decoupled outer-loop controllers with integral action, the combination providing a multivariable nonlinear PID and feedforward controller. The inner-loop neural-network controller is trained to achieve optimal performance behavior when future process behavior repeats the training experience. The outer-loop controllers compensate for process changes, unmeasured disturbances, and modeling errors. In the first and second embodiments, the neural network is used as an inner-loop controller in a process control system having a constraint management scheme which prevents integral windup by controlling the action of the outer-loop controllers when limiting is detected in the associated manipulated-variable control path. In the second and third embodiments, the neural-network controller is used without the integral controllers or the constraint management scheme as a simple PD feedforward controller.

Journal ArticleDOI
TL;DR: A nonlinear adaptive control is designed that guarantees asymptotic tracking of a desired angle reference signal, feeding back the whole state measurements (position, speed and currents), and may be used for preliminary on-line identification of those parameters that do not vary during operation.

Journal ArticleDOI
TL;DR: In this article, a neural network approach for online industrial tracking control applications is presented, which is characterized by the simplicity of its structure and its practical applicability for real-time implementation.
Abstract: This article presents a neural network approach for online industrial tracking control applications. In comparison to several existing neural control schemes, the proposed direct neural controller is characterized by the simplicity of its structure and its practical applicability for real-time implementation. In order to enhance the adaptive ability of the neural controller, a set of fuzzy rules is set up for selecting interim training targets. With minor qualitative knowledge about the plant, the scheme is designed for controlling the nonlinear behavior of the plant under conditions of disturbances and noise. Simulations of a ship course-keeping control under random wind forces and measurement noise have been investigated and comparison of performance has been made with a conventional PID controller. Results presented clearly demonstrate the feasibility and adaptive property of the proposed scheme. >

Journal ArticleDOI
TL;DR: The proposed fuzzy supervisor leads to promising results concerning the development of combined control structures and the comparison with a PID algorithm is a base for the design of the parallel PID-fuzzy controllers combination.

Patent
01 Nov 1995
TL;DR: In this article, the inner-loop PD controller employs a quasi-Newton iterative feedback loop structure whereby the manipulated variables are computed in an iterative fashion as a function of the difference between the inner loop setpoint and the predicted controlled variable as advanced by the optimum prediction time, to incorporate the downstream limiting effects on the non-limited control loops.
Abstract: A method and apparatus for a robust process control system which utilizes a neural-network based multivariable inner-loop PD controller cascaded with decoupled outer-loop controllers with integral action, the combination providing a multivariable nonlinear PID and feedforward controller. The inner-loop PD controller employs a quasi-Newton iterative feedback loop structure whereby the manipulated variables are computed in an iterative fashion as a function of the difference between the inner loop setpoint and the predicted controlled variable as advanced by the optimum prediction time, in order to incorporate the downstream limiting effects on the non-limited control loops. The outer-loop controllers compensate for unmodeled process changes, unmeasured disturbances, and modeling errors by adjusting the inner-loop target values.

Journal ArticleDOI
TL;DR: This paper investigates the possibility of training a neural network to behave in the same manner as an optimal ship guidance system, the objective being to provide a system that can adapt its parameters so that it provides optimal performance over a range of conditions, without incurring a large computational penalty.
Abstract: Many conventional ship autopilots use proportional integral and derivative (PID) control algorithms to guide a ship on a fixed heading (course-keeping) or a new heading (course-changing). Such systems usually have a gyrocompass as a single sensory input. Modern sea going vessels have a range of navigation aids most of which may be interconnected to form integrated systems. It is possible to employ the navigational data to provide best estimates of state vectors (Kalman filter) and optimal guidance strategies. Such techniques require powerful computing facilities, particularly if the dynamic characteristics of the vessel are changing, as may be the case in a maneuvering situation or changes in forward speed. This paper investigates the possibility of training a neural network to behave in the same manner as an optimal ship guidance system, the objective being to provide a system that can adapt its parameters so that it provides optimal performance over a range of conditions, without incurring a large computational penalty. A series of simulation studies have been undertaken to compare the performance of a trained neural network with that of the original optimal guidance system over a range of forward speeds. It is demonstrated that a single network has comparable performance to a set of optimal guidance control laws, each computed for different forward speeds. >

Journal ArticleDOI
01 Jul 1995
TL;DR: In this article, a PID controller design method based on process frequency response information is presented, in which the closed-loop performance is specified via the desired response of the control signal, and in the use of only one (for PI control) or two (for PID control) PID response points in the design.
Abstract: This paper presents a new PID controller design method based on process frequency response information. The novel ideas lie in the way that the closed-loop performance is specified via the desired response of the control signal, and in the use of only one (for PI control) or two (for PID control) process frequency response points in the design. Straightforward analytical formulas are given for the PID controller parameters. Simulation studies are given to compare this design method with other design methods found in the literature. The results indicate that the new method provides much smoother responses in both the control signal and process output, which are generally more desirable in the process control setting.

Journal ArticleDOI
TL;DR: It is proved that when a proportional integral (PI) controller is given, one can design a fuzzy logic controller whose output is identical to that of the PI controller.

Journal ArticleDOI
TL;DR: The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results.

Journal ArticleDOI
TL;DR: In this paper, the nonlinear characteristics of the turbine are first modelled as multiplicative uncertainties and an optimal robust governor is designed by taking into account such uncertainties explicitly, which guarantees the stability and the performance of the speed control loop for the entire turbine operating range.
Abstract: Design and analysis of a hydraulic turbine generator governor using optimal robust control methodology are presented. The approach is unique in the sense that the nonlinear characteristics of the turbine are first modelled as multiplicative uncertainties; and an optimal robust governor is designed by taking into account such uncertainties explicitly. The advantage of this approach is that the designed governor will guarantee the stability and the performance of the speed control loop for the entire turbine operating range. The performance of the new governor is shown to be superior to that of the conventional PID controller during large load disturbances. The resulting governor is only a third order. Hence, it has the same level of complexity as any other electrical-hydraulic governor in use today. >

Journal ArticleDOI
TL;DR: A new relay feedback identification method is proposed to find more accurate ultimate information by reducing the harmonics of the test signal so that thetest signal becomes more similar to the sinusoidal signal.
Abstract: The relay feedback method is one of the easiest identification methods to obtain the ultimate information of the process. Therefore, this method is widely used to automatically tune the PID controller in industry, and many applications of this method have been developed. It uses a square signal to obtain a continuous oscillatory response. However, since the square signal is totally different from the sinusoidal signal, the accuracy of the obtained ultimate information can be poor. Therefore, we propose a new relay feedback identification method to find more accurate ultimate information. The proposed method reduces the harmonics of the test signal so that the test signal becomes more similar to the sinusoidal signal.

Journal ArticleDOI
Pyung-Hun Chang1, D-S. Kim, K-C. Park1
TL;DR: In this article, a control law based on time-delay control has been derived for hybrid control, with a condition for closed-loop stability, and its relationship with the disturbance observer has been investigated.

Proceedings ArticleDOI
12 Sep 1995
TL;DR: The technique of genetic algorithms is proposed as a means of auto-tuning PID controllers by using on-line data and the genetic algorithm to identify a model of the process and off-line tune the PID controller so as to minimise a time-domain based cost function.
Abstract: The technique of genetic algorithms is proposed as a means of auto-tuning PID controllers. The technique involves firstly using on-line data and the genetic algorithm to identify a model of the process. Then the identified model, the genetic algorithm and simulation methods, are used to off-line tune the PID controller, so as to minimise a time-domain based cost function. Finally, the genetically tuned controller is implemented on-line on the real process. The results of the genetic auto-tuner are illustrated by auto-tuning a PID controller on a laboratory heat exchanger, and comparing the genetic auto-tuning technique with the Astrom-relay auto-tuning technique.

Journal ArticleDOI
TL;DR: In this paper, the integral time and the derivative time of a three-mode PID controller were expressed in terms of the time constant and dead time of the process, and the optimal tuning correlative formulas of the proportional gain for single and cascade control systems were obtained by the least square regression method.
Abstract: Design of one parameter tuning of three-mode PID controller was developed in this present study. The integral time and the derivative time of the controller were expressed in terms of the time constant and dead time of the process. Only the proportional gain was observed to be dependent on the implemented tunable parameter in which the stable region could be predetermined by the Routh test. Extension of the concept towards designing cascade PID controllers was straightforward such that only two parameters for the inner and outer PID controllers required to be tuned, respectively. The optimal tuning correlative formulas of the proportional gain for single and cascade control systems were obtained by the least square regression method.

Journal Article
TL;DR: In this paper, an artificial neural network (ANN) was used as a system identifier and as an intelligent controller for an air-handling system, where the ANN behaves as an identifier by continuously keeping track of all the real-time parameters associated with the whole air handling system.
Abstract: This paper reports the application of an artificial neural network (ANN) to serve both as a system identifier and as an intelligent controller for an air-handling system. A comprehensive software model has been established based on the specifications of a standard air-handling unit (AHU) on the market. The model is appropriate for testing various control algorithms including the new ANN identifier/controller. The ANN behaves as an identifier by continuously keeping track of all the real-time parameters associated with the whole air-handling system. Five actuating signals are produced based on the nonlinear error optimization of the outputs of the ANN, now served as a controller. The control target involves the minimization of two weighted factors--the errors between setpoints and control variables and the total energy consumption. The excellent performance of the ANN identifier/controller is illustrated by comparing it with that of a conventional proportional-integral-derivative (PID) controller.

Journal ArticleDOI
TL;DR: In this article, a linear mathematical model of a servovalve controlled pneumatic cylinder and a controller to control it are presented. But instead of deriving a complicated nonlinear mathematical model, the system identification technique was applied to obtain a linear time-invariant model.