Multivariable Generalized Predictive Control Research Articles

An improved multivariable generalized predictive control algorithm for direct performance control of gas turbine engine

Providing thrust for the aircrafts is the primary task of the gas turbine engine. Accurate and safe thrust controller design has always been the focus of the research. In this paper, an improved control algorithm for direct performance control of the gas turbine engine is proposed to realize the on-line estimation and tracking of the performance parameters. The controlled plant, identification module, controller and diagnosis module are integrated as a complete system, which has two loops. First, the adaptive model calculates the engine's performance parameters (thrust and compressor surge margin) as the feedback of the controller; then a Quantum-behaved Particle Swarm Optimization (QPSO) based multivariable generalized predictive controller (MGPC) is adopted as the main controller. To solve complex nonlinear optimization problems with constraints, the basic QPSO algorithm is improved from two aspects, global average best position and initial global best position. In addition, a penalty factor is added to the cost function to realize the limit protection of the rotor speed and exhaust gas temperature (EGT). Compared with the traditional control methods, the main contribution of this paper to realize the direct performance control without the conversion by the rotor speed, and the control system is responsible for limit protection and fault diagnosis in the meanwhile. Finally, simulations are performed to investigate the response of the performance parameters, the effects of the controller parameters, the ability of fault tolerance of the controller, and robustness against measurement noise and model uncertainties. The results show that the proposed scheme is robust and can achieve accurate regulation of the performance parameters and the limited outputs within the safe range, whether there is any fault or not.

Efficient Multivariable Generalized Predictive Control for Autonomous Underwater Vehicle in Vertical Plane

This paper presents the design and simulation validation of a multivariable GPC (generalized predictive control) for AUV (autonomous underwater vehicle) in vertical plane. This control approach has been designed in the case of AUV navigating with low speed near water surface, in order to restrain wave disturbance effectively and improve pitch and heave motion stability. The proposed controller guarantees compliance with rudder manipulation, AUV output constraints, and driving energy consumption. Performance index based on pitch stabilizing performance, energy consumption, and system constraints is used to derive the control action applied for each time step. In order to deal with constrained optimization problems, a Hildreth’s QP procedure is adopted. Simulation results of AUV longitudinal control show better stabilizing performance and minimized energy consumption improved by multivariable GPC.

Regulator configuration design by means of model predictive control

This paper presents new concepts and methods for regulator configuration design for stable and unstable multivariable systems. Nowadays the self-definitions of dynamic relative gain rarely consider the interaction influences of closed-loop controllers, and the interaction measurement can be also showed from the effects from controlled variables to manipulated variables through closed-loop controllers. Model Predictive Control (MPC) is an important multivariable centralized control strategy; by means of SFPC (State Feedback Predictive Control) and MGPC (Multivariable Generalized Predictive Control), two closed-loop interaction analysis methods are first put forward. Based on the control rate optimized, two inverse normalized gain arrays are obtained from SFPC and MGPC which show the dynamic effects of controlled variables on manipulated variables. With the inverse normalized gain arrays, the regulator configuration design of unstable systems can be carried out due to the effect of multivariable centralized control. Finally the advantages and effectiveness of proposed interaction analysis approaches are highlighted via several examples.

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

Application of adaptive multivariable Generalized Predictive Control to a HVAC system in real time

Abstract This paper presents the application of a Multivariable Generalized Predictive Controller (MGPC) for simultaneous temperature and humidity control in a Heating, Ventilating and Air- Conditioning (HVAC) system. The multivariable controlled process dynamics is modeled using a set of MISO models on-line identified from measured input-output process data. The controller synthesis is based on direct optimization of selected quadratic cost function with respect to amplitude and rate input constraints. Efficacy of the proposed adaptive MGPC algorithm is experimentally demonstrated on a laboratory-scale model of HVAC system. To control the airconditioning part of system the designed multivariable predictive controller is considered in a cascade dual-rate control scheme with PID auxiliary controllers.

Automatic performance estimation of conceptual temperature control system design for rapid development of real system

This paper presents an automatic performance estimation scheme of conceptual temperature control system with multi-heater configuration prior to constructing the physical system for achieving rapid validation of the conceptual design. An appropriate low-order discrete-time model, which will be used in the controller design, is constructed after determining several basic factors including the geometric shape of controlled object and heaters, material properties, heater arrangement, etc. The proposed temperature controller, which adopts the multivariable GPC (generalized predictive control) scheme with scale factors, is then constructed automatically based on the above model. The performance of the conceptual temperature control system is evaluated by using a FEM (finite element method) simulation combined with the controller.

A multivariable multiobjective predictive controller

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

Direct Algorithm for Multivariable Generalized Predictive Controller0s Coe±cients of Diagonal CARIMA Model

A direct way for getting controller 0 s coe-cents of multivariable generalized predictive control (MGPC) of diagonal CARIMA model is developed in order to simplify the design and implementation of MGPC. A very concise MGPC controller is obtained by directly manipulating the model predictor of a diagonal multivariable CARIMA model recursively, and the control moves are the product of the controller 0 s coe-cients and set-points, historical input/output data of the plant and predictive errors of the predictor. The controller's coe-cients are determined only by the model parameters and design parameters, and the number of coe-cients only depends on the orders of the model. This method avoids solving Diophantine equations, reduces the computational overhead on line, and simplifles the implementation of MGPC. Its validity is demonstrated by comparative experiment results obtained from a nonlinear liquid system controlled by DCS.

PROPERTIES OF MGPC DESIGNED IN STATE SPACE

This paper presents some properties regarding the Multivariable Generalized Predictive Controller (MGPC) designed in state space. The authors proposed the design of such a controller under the condition that it gives the same results as the input/output (I/O) formulation of the GPC proposed originally by Clarke. The main reason for redesigning the controller in state space is the power of the analysis tools available. These tools allow some properties to be treated more easily than under (I/O) formulation. These properties include: stability, robustness, closed loop representation, specifications, etc. This paper presents results related to: observability and controllability of the CARIMA model used, closed loop representation, uniqueness and the existence of control law without constraints and closed loop stability without constraints.

Robust design of multiloop GPC and its application for a cement mixing plant

In this paper, a design scheme of generalized predictive control(GPC) systems is proposed for multivariable systems. Generally, the multivariable GPC does not only require the huge amount of calculations, but also make the design of the control weighting factor included in the GPC criterion difficult. The proposed scheme firstly adopts a static pre-compensator as an approximately decoupling device, in order to roughly decouple the controlled system. Furthermore, by regarding the roughly decoupled system as plural single-input single-output subsystems and taking account of robust stability for uncertainties, a single-input single-output GPC system is designed for each subsystem. Owing to this procedure, one can easily design each GPC system and the amount of the required calculations is much less than that of the conventional multivariable GPC.

A new decoupling design of self-tuning multivariable generalized predictive control

A novel robust decoupling method with multivariable generalized predictive control (MGPC) for a class of nonlinear systems is presented in an adaptive version. The cross-coupling action and the non-linear actors of the system are identified on-line by a neural network. A feedforward compensation based on generalized predictive control, is proposed for decoupling control. A modified recursive least-squares (RLS) algorithm can be used to estimate the linear parameters for time-varying systems. Simulations are carried out and the results show the effectiveness of the proposed algorithm. Copyright © 1999 John Wiley & Sons, Ltd.

On the link between generalized predictive control and deadbeat control for linear multivariable systems

This paper studies a special case on multivariable generalized predictive control (GPC) in which a deadbeat controller is obtained. It is shown that the multivariable GPC design can be made to be equivalent to the deadbeat control design by certain choices of the design parameters. The proposed procedure needs the introduction of different control horizons for different inputs. A tuning strategy is used to achieve deadbeat control for real-time implementation.

Role of input weighting and bounding in the characteristic GPC method

Transcribing the Characteristic decomposition of a transfer function matrix of a discrete-time multivariable system from the frequency domain to the time domain provides a convenient means of breaking down complex multivariable GPC problems into a set of simple scalar Characteristic Generalized Predictive Control (GPC) problems. The difficulty is however, that in so doing one ends up costing the activity of a ‘fictitious’ set of inputs in place of the inputs into the actual systems. It is the purpose of the present paper to overcome this difficulty and to propose suitable extensions. Another aspect of the paper is to consider the means of bounding inputs from ‘below’ in order to avoid non-minimum phase behaviour (which is inherent in GPC) and from above in order to avoid ‘overfiring’ which could come into conflict with physical/economic constraints. The paper proposes some suitable extensions that overcome these problems. The efficacy of the results of the paper are demonstrated by means of two illustativ...

Adaptive generalized predictive controller for MIMO systems

The multivariate generalized predictive controller (GPC) is studied (Clarke 1987a, Shah 1987). The multivariable GPC algorithm is presented in a stochastic framework, and a model-following capability is introduced in the control law. The method is described for a controlled autoregressive integrated moving average (CARIMA) model, and for the equivalent state-space representation in its innovation form. The closed-loop system equations are derived and analyzed, using the state-space approach.