Unified Predictive Control Research Articles

Cooperative Control for Dual Permanent Magnet Motor System with Unified Nonlinear Predictive Control

In order to improve the position tracking precision of dual permanent magnet synchronous motor (PMSM) systems, a unified nonlinear predictive control (UNPC) strategy based on the unified modeling of two PMSM systems is proposed in this paper. Firstly, establishing a unified nonlinear model of the dual-PMSM system, which contains uncertain disturbances caused by parameters mismatch and external load changes. Then, the position contour error and tracking errors are regarded as the performance index inserted into the cost function, and the single-loop controller is obtained by optimizing the cost function. Meanwhile, the nonlinear disturbance observer is designed to estimate the uncertain disturbances, which is used for feed-forward compensation control. Finally, the proposed strategy is experimentally validated on two 2.3 kW permanent magnet synchronous motors, and the experimental results show that effectiveness and feasibility of proposed strategy.

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

Predictive current control (PCC) applied on permanent magnet synchronous motors (PMSMs) has been developed into mainly three methods: the conventional finite-control-set PCC, the double voltage vectors PCC, and deadbeat PCC. However, each approach has its particular calculation way for voltage vectors selection and respective execution duration. This paper, based on the deadbeat idea, presents a unified predictive current control scheme of PMSMs. Under this scheme, the prior three classes are able to be clearly unified into one frame with lower calculation effort. Furthermore, to cope with problem of parameter mismatch in dq-axis current predictive model, a integrated identification method is proposed. Firstly, data selectors are designed to reject abnormal data of sampling signals, and then the interval-varying multi-innovation least squares algorithm is combined with forgetting factor (V-FF-MILS) to approximate the error terms caused by electromagnetic parameters error. The estimated results are online fed to the model of PMSM to enhance its accuracy. Finally, the processor in loop (PIL) simulation results verify that the proposed integrated scheme has advantages in current control of PMSMs with large-scale parameter uncertainty.

Adaptive unified predictive control applied to first-order-plus-dead-time processes to eliminate unknown class of deterministic disturbance

A novel adaptive unified predictive control method (adaptive-UPC) is proposed and applied to the first-order-plus-dead-time (FOPDT) process models with model uncertainties (gain, time constant and dead-time) under unknown deterministic disturbances (step, ramp and the unknown frequency sinusoid). Based on indirect self-tuning regulator, this new control structure is constructed by combining UPC controller, recursive least-squares estimation with forgetting factor (RLS with forgetting factor) and variable regression estimation (VRE). By the simulation results, this scheme is proven to be applied to such process model with model uncertainties effectively.

Design Method for Predictive Cascade Control Structures

The paper describes an aspect of advanced control techniques applied in cascade control structure. The cascade control structure has been developed by considering UPC (Unified Predictive Control) pole-placement controllers, calculated taking into account the dynamical separation of the control loops through an adequate choice of the objective functions. Aspects of designing, problems encountered and performance of the control system are dealt with and illustrated by simulation results. The main achievement is a design algorithm for polynomial predictive cascade structures used in motion control, under the assumption that the control loops are dynamically separated. The design method was tested on a predictive cascade structure for a servo application.

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.

Implementation of a predictive controller for thermal treatment processes

This paper is concerned with multivariable predictive control and its implementation in thermal treatment processes. The choice of a control structure that ensures decoupling is discussed. A unified predictive controller is used to implement the monovariable control systems for each channel of the decoupled plant. Aspects of the modelling and performance of the control scheme are dealt with and illustrated by plant results. The model of the thermal treatment processes has been used to check the controller behaviour before its real implementation and some simulation results are also given.

Implementation of a Predictive Controller for Thermal Treatment Processes

This paper is concerned with multivariable predictive control of thermal treatment processes and its implementation. The choice for a control structure ensuring decoupling is discussed. A unified predictive controller is used to implement the monovariable control systems for each channel of the decoupled plant. Aspects of the modelling and performance of the control scheme are dealt with and illustrated by plant results. The model of the thermal treatment processes has been used to check the controller behaviour before its real implementation and some simulation results are given too.

Application of Unified Predictive Control to On/Off Control of Hydraulic System Driven by Fast-Switching Solenoid Valves

The utilization of fast-switching solenoid valves permits the construction of lowcost hydraulic systems with high resistance to oil contamination. In spite of these advantages, the use of solenoid valves results in severe nonlinearities and discontinuities in the flow rate, making it difficult to control and design the system. In this study, the performance of a hydraulic system operated by an on/off unified predictive controller was investigated. It was verified that despite strong nonlinearities in the plant, the performance of the system was considerably improved by employing the predictive control scheme.

Adaptive Predictive Controller for Hydraulic Systems Driven by Fast Switching On/Off Solenoid Valves

In hydraulic systems driven by fast-switching solenoid valves, the utilization of conventional adaptive predictive controllers usually requires much computational effort, and thus it is difficult to realize real-time implementations in plants using short sampling times. This paper deals with an adaptive unified predictive controller which can be implemented conveniently for hydraulic systems driven by fast-switching solenoid valves. It is clarified by experimental investigation that the amount of computational effort is considerably reduced by employing the proposed controller. It is also clarified that the controller permits convenient and easy implementation of an adaptive predictive control scheme for on/off tracking control systems.

Application of Unified Predictive Control (UPC) for an Electro-Hydraulic Servo System

There is a strong demand in practical fields to obtain better control performance by means of a more effective utilization of future information. One useful method for improving the dynamic performance of a control system is to utilize the future desired signal if possible. Recently, a synthesis method for unified predictive control, called UPC, has been developed by Soeterboek and coworkers. It can be said that by this method the unification of several well-known predictive control methods has been accomplished. The purpose of this study is to find out whether the UPC controller is applicable to an electro-hydraulic servo-system. A hydraulic system with the UPC controller was designed and the performance of the system was investigated by experiments and computer simulation. It was verified that the performance of the system was improved considerably by employing this synthesis method.

Multivariable process and prediction models in predictive control—A unified approach

AbstractIn this paper multivariable generalizations of the SISO unified process model (UPCM) and unified prediction model (UPM) used in the SISO unified predictive controller are presented. the necessary assumptions regarding the multivariable UPCM (MUPCM) are established. the properties of the multivariable UPM (MUPM) are examined and the conditions for a zero steady state prediction error are established.The MUPCM has a general noise model and unites all multivariable, discrete, linear process models in transfer function form. the MUPM uses an output filter which unites all relevant output filters used in predictive controllers. Furthermore, the elements of the output can be predicted over different horizons. the MUPCM and MUPM and the algorithm used to calculate the MUPM are chosen such that the predictions of the outputs can be calculated as fast as possible, not only for the general model, but also for special cases such as ARX, ARIMAX, Box‐Jenkins and FIR models.

A predictive controller for the Mach number in a transonic wind tunnel

The use of the unified predictive controller in controlling the Mach number of a transonic wind tunnel is examined. It is shown, by means of simulations and experiments, that the unified predictive controller yields an overall performance improvement of 30-60% in comparison with the proportional-integral-derivative (PID) controller that is normally used to control the Mach number. The rejection by the unified predictive controller of disturbances caused by changing the angle of attack is a factor of four better than that for the PID controller. This makes it possible to change the angle of attack faster, so that the efficiency of the wind tunnel operation can be improved. It is believed that this is mainly due to the fact that knowledge about the disturbances can be incorporated into the predictive controller design. Simulations have shown that even better results can be expected; the results have not yet been verified by experiments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

On the Design of the Unified Predictive Controller

In this paper the design parameters of the Unified Predictive Controller are discussed. It is shown that separate tuning of the servo and regulator behavior can be obtained by a special choice of the design parameters. Rule of thumb methods are presented that can be used in order to obtain initial settings for the design parameters.