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.     

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.     

The effect of thermal processing and storage on the physicochemical properties and in vitro digestibility of potatoes

Shepody potatoes were cooked using three common cooking methods – microwaving, boiling and pressure cooking. Microwaving for 2.5 min retained the highest amounts of slowly digestible starch (SDS, 19.6%) and resistant starch (RS, 48.8%) as compared to the other cooking treatments. Similarly, enthalpy and FTIR results (ratio of 1047/1022 cm^?1) were also higher for microwaved samples, again indicating incomplete gelatinisation. Potatoes were also boiled for 15, 30 or 45 min, followed by 1, 3 or 7 days retrogradation at 23 or 4 °C. Retrogradation enthalpy increased significantly (P ≤ 0.05) with increased storage time and decreased storage temperature; FTIR results displayed temperature dependency; at 4 °C, ordered structure increased with increasing storage time, whereas the opposite trend was seen at 23 °C. Lastly, formation of SDS and RS was favoured for longer boiling times (30 – 45 min), extended storage times (3–7 days) and 4 °C.     

Genetic determinants of diastolic and pulse pressure map to different loci in Lyon hypertensive rats

Several genetic loci involved in blood pressure regulation have recently been localized in experimental models of hypertension, but the manner in which they influence blood pressure remains unknown. Here, we report a study of the Lyon hypertensive rat strain showing that different loci are involved in the regulation of steady-state (diastolic pressure) and pulsatile (systolic-diastolic, or pulse pressure) components of blood pressure. Significant linkage was established between diastolic blood pressure and a microsatellite marker of the renin gene (REN) on rat chromosome 13, and between pulse pressure and the carboxypeptidase B gene (CPB) on chromosome 2. These findings show that two independent loci influence different haemodynamic components of blood pressure, and that pulse pressure has a specific genetic determination.     

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.     

On simplified predictive control as a generalization of least-squares dynamic matrix control

Simplified predictive control (SPC) of a single-input single-output control scheme is compared to the more sophisticated, least-squares formulation of dynamic matrix control (DMC) and its move-suppressed variant (move-suppressed DMC) for a typical two time-step control horizon. A closed-loop, continuous analysis shows that the discrete form of SPC generalizes the discrete DMC algorithm, and its variants, to control responses faster than one-half the process response time while remaining well conditioned.     

Well-conditioned model predictive control

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.     

Development of characteristic equations and robust stability analysis for MIMO move suppressed and shifted DMC

Discrete-time controller and closed-loop transfer functions were developed for move suppressed λ and the recently formulated m-shifted multiple-input-multiple-output (MIMO) dynamic matrix control (DMC). Using these transfer functions, robust analyses were conducted for MIMO plants by varying corresponding delay and gain ratios of the system. In all instances, robust plots indicate that the shifted DMC is less sensitive and hence more robust to variations in the plant parameters than move suppressed DMC. It was shown that the design of these MIMO DMC controllers depends on the plant closed-loop performance and overall stability, since the selection of λ and m directly influences the plant robustness and closed-loop dynamics.