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In an industrial coke process, the dynamic relationship between the input and output devices is complicated. Since performance is closely related to the control used, improved control alternatives are necessary. This paper deals with the oxygen content control loop and proposes an improved state-space model predictive control (MPC) aimed at satisfying steady air supply into the process. The details of the proposed MPC are first described and tested on a typical example and then implemented on an industrial coke furnace. Simulation examples and real-time implementation results both show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a new design method of model predictive control (MPC) based on extended non-minimal state space models, in which the measured input and output variables, their past values together with the defined output errors are chosen as the state variables. It shows that this approach does not need the design of an observer to access the state information any more and by augmenting the process model and its objective function to include the changes of the system state variables, the control performances are superior to those of the controller that does not bear this feature. Furthermore, closed-loop transfer function representation of the model predictive control system facilitates the use of frequency response analysis methods for the nominal control performances of the system. [Copyright &y& Elsevier]

The paper presents a combination modeling procedure and the implementation of a nonlinear predictive control scheme for the optimization of industrial chemical processes. The model structure is first based on a simple step response method. This provides a way to use prior knowledge about the dynamics, which has a general validity, while additional information about the process behavior is derived from measured plant data-model error. This data error driven model framework is applicable for a wide range of chemical operating units under a certain control policy. The same idea is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the modeling training algorithm and the nonlinear predictive control approach are demonstrated through a coke furnace case study. A good model fitting for the nonlinear plant is obtained by using the new method. A comparison with traditional approaches shows that the new algorithm can considerably reduce modeling error and improve control accuracy. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened. [Copyright &y& Elsevier]

This note shows that the solvability of the modified quasi-min–max MPC algorithm of the paper Lu and Arkun, Automatica 36 (2000) 527 for the systems with input constraints is not guaranteed and the proof of the theorems are not sufficient via a counterexample of the Remark 5. [Copyright &y& Elsevier]

Actuatorfaults commonly exist in process control systems. Dueto these faults, the controller may not achieve the required target,so the control performance may degrade. In this paper, the fault-tolerantcontrol is designed through a predictive functional control frameworkto deal with partial actuator faults and unknown disturbances, whichexist widely in process control systems. Based on a new state spaceformulation of the process models, an improved predictive functionalcontrol scheme is proposed, where satisfactory closed-loop controlperformance is achieved even with unknown disturbances and actuatorfaults. With the actuator faults, the system becomes a process withparameter uncertainties. Hence, a sufficient condition that guaranteesclosed-loop robust stability is presented. Simulations are given toillustrate the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the application of a linear iterative design of nonlinear predictive functional control for chamber pressure of industrial coke furnace. The control target is to maintain the chamber pressure of a coke furnace within a set range while rejecting the effect of disturbances (mainly the coke removing process and the switch of coke towers).The proposed control is easy to implement, as it selects a simple structure and a linear iterative approach instead of nonlinear control schemes to improve control performance in such a way that results compared with traditional PID controllers are improved. Application of industrial plant demonstrates the efficiency of the method. [Copyright &y& Elsevier]