Partial State Reference Model Research Articles

Multivariable Q-parametrization for Rejection of Harmonic Disturbances

The design of accurate control becomes more difficult due to the complexity of industrial processes. In this paper, we are interested on the tracking of reference trajectories and the rejection of harmonic disturbances for discrete multi-input multi-output systems. A new control strategy based on the combination of partial state reference model control, internal model principle and Youla-Kucera parametrization for discrete multivariable systems isproposed. A numerical example shows that the proposed strategy gives a good performance in terms of rejecting harmonic disturbances and reference trajectories tracking.

Global predictive control: A unified control structure for decoupling setpoint tracking, feedforward compensation and disturbance rejection dynamics

Predictive control has now reached a mature level. However, a unification of long-range predictive controllers, such as unified predictive control (UPC), generalised predictive control (GPC) and partial state model reference control (PSMRC) is still lacking. The paper presents global predictive control (GlobPC), which unifies, for stable processes, these controllers. GlobPC is based on an internal model framework where three independent control criteria are minimised: one for tracking, one for regulation and one for feedforward. Using three criteria allows for the desired tracking to be obtained, and for regulation and feedforward to behaviours in an optimal way, while keeping them perfectly separated. The GlobPC structure also exhibits a clean separation of the stochastic and deterministic parts of the control algorithm. The state–space model representation is used for a more uniform and easy programming of GlobPC for both single-input single-output and multiple-input multiple-output processes. An application to a pilot plant illustrates the benefits of the method.

EDF steam generator benchmark: The partial state reference model control approach

This paper presents a robust predictive control approach to a benchmark problem involving water level control in the steam generator of a nuclear power reactor. Control design is performed using a sensitivity function shaping procedure which broadly borrows from the robust control culture. Of fundamental interest, the control design model is obtained using an experimental identification approach developed from established results in system identification.

Partial state reference model adaptive control of batch and fed-batch chemical reactors

This paper presents a study where an adaptive partial state reference model (PSRM) controller is designed and experimentally implemented for temperature control in batch and fed-batch chemical reactors. The PSRM control design is carried out using a linear quadratic control within the delta operator formulation. The control allows to specify the tracking and regulation dynamics independently, while the delta formulation is motivated by the convergence of its performances to their continuous counterpart as well as its intrinsic numerical robustness.

ADAPTIVE LINEAR QUADRATIC CONTROL OF BATCH AND SEMI-BATCH CEHMICAL REACTORS WITHIN THE DELTA OPERATOR FORMULATION

Batch and semi-batch chemical reactors are extensively used in polymer, fine chemical and pharmaceutical industries. The temperature control is of fundamental importance from product quality and reproductibility points of view. It is then necessary to improve the automation of these plants which are typically controlled using standard PID controllers. An autotuning PID controller is proposed and evaluated by application to a batch and semi-batch pilot plant reactor to investigate advanced control techniques. The involved control design is derived in the spirit of the partial state reference model adaptive control (PSRMAC) approach within the delta operator formulation. PSRMAC is mainly motivated by its suitable tracking capability when only a crude knowledge about the plant to be controlled is available. The delta operator formualtion is particularly motivated by the convergence of the performances to their continuous time counterpart as well as its numerical robustness when the sampling is required to be fast.

Position/Force Control of a Flexible Robotic Structure Using Generalized Predictive Control (GPC)

This paper presents some results achieved in the position/force control of a flexible robotic structure in both free and constrained movements. The control has been developed using Generalized Predictive Control (GPC) with a partial state reference model. After describing the flexible structure and a brief introduction to OPC, the paper deals with the controller design for both position and force tasks. Some simulation results are presented and discussed. A hybrid position/force control scheme was chosen for the constrained position/force tasks. The supervisor in charge of choosing the adequate control strategy is described before presenting some results obtained in the experimental trials.

The Partial State Reference Model Control Relevant Identification

Abstract This paper presents an iterative partial state reference model (PSRM) control relevant identification scheme designed within the delta operator formulation. The motivation of this iterative method is to carry out a joint design of identification and control with the intention of improving performances and stability robustness. The PSRM: control design, using a generalized predictive control approach, is motivated by its tracking performance. An experimental evaluation involving a flexible transmission system is made to demonstrate the applicability of the considered PSRM control system.

Adaptive control—practical aspects and application to a grinding circuit

This paper presents practical aspects and an application of the decentralized partial state reference model adaptive control (DPSRMAC) to a grinding circuit. This control algorithm belongs to the class of long-range predictive controllers. Having in mind the pole placement approach, the quadratic control objective is expressed in terms of input and output tracking errors. The regulation and servo performances can be specified independently. A robust parameter estimation algorithm is used for on-line identification of the single-input/single-output (SISO) models. Simulations have been carried out using a phenomenological model of a grinding circuit. Comparisons are made between the DPSRMAC and decentralized SISO extended PI controllers, showing the efficiency and robustness of the adaptive control algorithm. © 1997 John Wiley & Sons, Ltd.

PID adaptive control of the temperature in batch and semi-batch chemical reactors

In this paper, a digital PID controller, derived in the partial state reference model (PSRM) adaptive control spirit, is used to control the temperature in batch and semi-batch chemical reactors. This controller consists in the combination of a performance-oriented linear quadratic control design method and a robust parameter estimator within the delta operator formulation. Such a formulation is particularly motivated by the convergence of the performances to their continuous time counterpart as well as its numerical robustness when the sampling is required to be fast. Experiments on batch and semi-batch reactors are carried out to emphasize the performances of the aforementioned controller with respect to those of an auto-calibrated PID controller.

On Some Advanced Control Techniques

The motivation of this paper is to present the partial state reference model (PSRM) adaptive control approach and investigate its applicability to diesel engines which are widely used as power sources. The control objective consists in well damped and offset-free response to speed commands in spite of the well known complexity of the engine dynamics. This particularly allows to improve the fuel efficiency.

A pole placement adaptive control of a rapid thermal processor

Rapid thermal processing (RTP) techniques are often applied in microelectronic technology. The main characteristics of RTP techniques lie in single wafer treatments, fast thermal kinetics at low thermal cost and reduced contamination risks; moreover, many types of reaction can be realized with one single wafer treatment. Problems of measurement and control of space-time temperature profiles have not yet been solved in a satisfactory way; in particular, classical control algorithms (such as PID control) can give bad dynamics to the system if nominal temperature profiles must be modified. Modelling of the thermal behaviour of the process leads to very complex mathematical models. These are the reasons why adaptive control techniques are chosen. A CARIMA model is an interesting input-output model of the thermal processor, whose parameters can be estimated on-line, using an identification scheme which includes supervisory actions. The adaptive control algorithm is a full-state feedback pole-placement law with an adaptive observer. A partial-state reference model was included in the control law algorithm. This type of control law can be implemented even for non-minimum-phase systems, time-varying dynamics, a wrong assumption about the time delay of the system, and when disturbances on the load exist. Experimental results are presented which show a better performance when using an adaptive control strategy

Experimental Evaluation of Partial State Reference Model Control Relevant Identification

This paper presents an iterative partial state reference model (PSRM) control relevant identification scheme designed within the delta operator formulation. The PSRM control design is motivated by its tracking performance and carried out using an LQG method. An experimental evaluation involving a flexible transmission system is made to demonstrate the applicability of the considered PSRM control system.

Adaptive control of a flexible transmission system

Abstract This paper presents a set of real-time experiments conducted to demonstrate the performances of the partial state reference model adaptive control on a flexible transmission system. Particular emphasis is put on the fundamental issues that arise when applying the adaptive control to real plants, namely the tracking capability, the adaptation alertness and robustness with respect to state disturbances and reduced-order modelling.

Partial state reference model (adaptive) control of a benchmark example

This paper presents two solutions to the IFAC robust/adaptive benchmark example involving the partial state reference model (adaptive) control approach proposed in M'Saad, M., I. D. Landau, M. Duque and M. Samaan (1989). International Journal of Adaptive Control and Signal Processing, 3, 155–165, and M'Saad, M., I. D. Landau and M. Samaan (1990). International Journal of Adaptive Control and Signal Processing, 4, 133–148. These solutions are based on a nominal plant model which is obtained with a least squares algorithm under a suitable experimental planning. A particular emphasis is put on the possibility to improve the performance while considerably simplfying the control design by properly exploiting all the information about the plant to be controlled.

Multivariable generalized predictive adaptive control with a suitable tracking capability

The usefulness of the generalized predictive control design has been demonstrated over the last decade from both theoretical and practical standpoints. Indeed generalized predictive control can be configured to behave identically to many existing long-range predictive control strategies. Of practical importance, the underlying tuning-knobs allow the performances to be easily tailored to the user's specifications. This paper is concerned with a multivariable generalized predictive adaptive controller with partial state reference model tracking capability. Simulation studies, involving a binary distillation column model, are carried out to emphasize the performance of the proposed adaptive controller in tracking as well as in regulation.

Predictive temperature tracking for batch evaporative crystallizers: the partial state model reference approach

Batch operated crystallizers are widely used in the processing of fine chemical and pharmaceutical products. However, the tracking of various temperature setpoint trajectories appears to be a very difficult task if the crystallizer is cooled through vacuum, and advanced closed-loop control strategies have to be considered. This paper describes the application of the so called partial state model reference generalized predictive control approach (PSMR GPC) to the crystallization of organic compounds. After a brief introduction to the processes of crystallization from the solution, the main difficulties related to the use of vacuum for cooling industrial crystallizers are emphasized, followed by the weaknesses of PI control. The PSMR strategy is recalled and this algorithm is applied to calculate the input pressure leading to satisfactory tracking of temperature trajectories. The design of a non-linear adaptive supervisor is then described. In addition to the basic control law, this supervisor has been implemented to ensure improved safety and robustness. Supervisory PSMR GPC performances are reported. With respect to the main industrial constraints and requirements, the results are encouraging.

Partial State Reference Model (Adaptive) Control of a Benchmark Example

This paper presents two solutions to the IFAC 93 adaptive/robust benchmark example involving the partial state reference model (adaptive) control approch proposed in M'Saad et al. (1989, 1990). These solutions are based on a nominal plant model which is obtained with a least squares algorithm under a suitable experimental planning. A particular emphasis is put on the possibility to improve the performance while considerably simplifying the control design by properly exploiting all the information about the plant to be controlled.

Linear and non‐linear adaptive control of alcoholic fermentation process: Experimental results

AbstractThis paper deals with the experimental implementation of several control algorithms on a continuous flow fermentation process. The regulation and tracking problems of the substrate concentration are considered. Our objective is to show the advantages and drawbacks of these algorithms in tracking and regulation behaviour, overshoot, knowledge contribution, number of tuning parameters, etc. The different controllers described in this paper are the PI controller, the non‐linear adaptive L/A algorithm, the linear adaptive controller (predictive control with partial state reference model) and the adaptive controller based on the non‐linear structure of the process model (pole placement control). These controllers are applied to a pilot‐scale fermentation system with satisfactory results.

Partial state model reference adaptive control of a rapid thermal processor

AbstractThe temperature control problem in a rapid thermal processor is addressed using first a conventional PID controller and then a partial state model reference adaptive controller derived via the long‐range predictive control and linear quadratic control approaches. These two algorithms are shown to provide such fundamental design features as offset‐free disturbance rejection and stability robustness with respect to unmodelled and time‐varying dynamics.

Partial state reference model adaptive control of multivariable systems

In this paper, the authors aim at designing a partial state reference model adaptive controller for multivariable systems. The partial state reference model concept is particularly used to incorporate an appropriate tracking capability into the linear quadratic control design. Simulation studies, involving a binary distillation column model, are carried out to emphasize the performances of the proposed adaptive controller.