A nonlinear regression model-based predictive control algorithm.

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

Application of infinite model predictive control methodology to other advanced controllers.

This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.

Real-time comparison of a number of predictive controllers.

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost-function. This paper presents the newer MPC schemes such as extended predictive control (EPC) and shifted MPC as well as other well known forms. The control performance of these controllers are compared using two systems that are slow and fast reacting. The closed-loop responses are compared and the differences and similarities are explained on the basis of the structure of the control schemes. Disturbance rejection and the tracking of various setpoint trajectories are performed with good closed-loop results from all the controllers. It was found that the controllers that were specifically designed to reduce the system matrix ill-conditionality such as EPC and generalized predictive control provided better control performance when compared to other MPC methods.

Properties of extended predictive control.

Many tuning strategies for model predictive control algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost function. In this paper, the properties of a new predictive controller termed extended predictive control (EPC) are investigated and presented. These properties are important to the understanding of the unique tuning strategy of EPC. EPC is based on the assumption of infinite horizon which is preferable to guarantee stability. The EPC properties are derived using a second order plant with relatively large dead time and is applicable to any open-loop stable system. The tuning strategy of EPC was applied to generalized predictive control with good results.

MIMO extended predictive control-implementation and robust stability analysis.

The objective of this work is to develop a new tuning strategy for multivariable extended predictive control (EPC). A natural concern is the problem of ill conditionality in controlling multi-input multi-output (MIMO) systems. The main advantage of EPC is that it has a simple and effective tuning strategy that results in a well-conditioned system which can achieve tight closed-loop response. Moreover, unlike most existing model predictive control tuning strategies, the proposed strategy establishes a direct relationship between one main tuning parameter for each subprocess of the MIMO system. This tuning method has been derived based on the assumption of an infinite control horizon resulting in powerful stability for the nominal case and in the presence of model uncertainty. This tuning method is applicable to unconstrained multivariable processes, and was proven to have good control on nonsquare systems. The main features of the new tuning strategy are practically illustrated on a MIMO temperature system with improved control performance as compared to move suppressed predictive control.

SISO extended predictive control: implementation and robust stability analysis.

The proposed algorithm of extended predictive control (EPC) represents an exact method for removing the ill-conditioning in the system matrix by developing a unique weighting structure for any control horizon. The main feature of the EPC algorithm is that it uses the condition number of the system matrix to evaluate a single tuning parameter that provides a specified closed-loop response. Robust analysis demonstrated that EPC is more robust in comparison with move-suppressed and m-shifted predictive controllers in all aspects of process variation in gain, delay, and time-constant ratios. Tuning of EPC is effective and simple since there is a direct relationship between closed-loop performance and its tuning parameter.

SISO extended predictive control-formulation and the basic algorithm.

A new predictive controller is developed that represents a significant change from conventional model predictive control. The method termed extended predictive control (EPC) uses one tuning parameter, the condition number of the system matrix to provide an easy-to-follow tuning procedure. EPC drastically improves the system matrix conditionality resulting in faster closed-loop response without oscillatory transients. The control performance of EPC is compared with the original move suppressed and recently derived shifted predictive controllers, with improved results.