Modeling and control of Wiener-type processes

We develop a simple relay feedback method to identify Wiener-type nonlinear processes. It separates the identification problem of the nonlinear static function from that of the linear dynamic subsystem to simplify the identification procedure significantly. Owing to the separation, the unmeasurable output of the linear dynamic subsystem can be obtained in a straightforward manner. Then, determining the model structure of the nonlinear static function becomes very simple and the estimates are robust to additive output noises. We can identify the whole activated region of the nonlinear static function as well as the ultimate information of the linear dynamic subsystem from only one relay feedback test. More information on the linear dynamic subsystem can be estimated by well-established linear system identification methods from additional tests. We use a nonlinear control strategy to compensate the nonlinear dynamics of the Wiener process so that the design parameters can be determined by usual tuning rules developed for linear processes and a high control performance can be achievable as in linear processes.     

A model predictive formulation for control of open-loop unstable cascade systems

Cascade control is commonly used in the operation of chemical processes to reject disturbances that have a rapid effect on a secondary measured state, before the primary measured variable is affected. In this paper, we develop a state estimation-based model predictive control approach that has the same general philosophy of cascade control (taking advantage of secondary measurements to aid disturbance rejection), with the additional advantage of the constraint handling capability of model predictive control (MPC). State estimation is achieved by using a Kalman filter and appending modeled disturbances as augmented states to the original system model. The example application is an open-loop unstable jacketed exothermic chemical reactor, where the jacket temperature is used as a secondary measurement in order to infer disturbances in jacket feed temperature and/or reactor feed flow rate. The MPC-based cascade strategy yields significantly better performance than classical cascade control when operating close to constraints on the jacket flow rate.     

Decentralized fault-tolerant control system design for unstable processes

Fault-tolerant control is an important issue in control of mission critical processes. In this paper, a new approach to fault-tolerant control of unstable processes is proposed based on the Passivity Theorem. The control system is designed in two sequential steps: A multi-loop proportional controller is used to stabilize the unstable process; a passivity-based decentralized unconditionally stabilizing (DUS) controller is then applied to the stabilized process. While the multi-loop stabilizing controllers need to be built with redundancy, the DUS controller is inherently fault tolerant and can maintain closed-loop stability when any of its loops fail. By using a stabilizing proportional controller with the fewest loops, control redundancy can be reduced to the minimum level.     

Use of bifurcation analysis for development of nonlinear models for control applications

Nonlinear system identification poses challenging questions because a closed general theory is not available for this field. Particularly, nonlinear models based on neural networks (NN) may present incompatible general dynamic process behavior, leading to improper closed-loop responses, even when they allow for satisfactory one step ahead prediction of process dynamics, as required by traditional validation methods. It is shown here that performing detailed bifurcation and stability analysis may be very helpful for the adequate development and implementation of nonlinear models and model based controllers. The study of many parameters that are defined a priori during the training of the NN shows that the spurious dynamic behavior is related mostly to the use of incomplete data sets during the learning process. This is an indication that, for each kind of process, the number, range and distribution of the data points in the operation region of interest are of paramount importance for proper training of the nonlinear model. Strategies to improve the quality of the training procedure are provided and analyzed both theoretically and experimentally, using the solution polymerization of styrene in a tubular reactor as a case study.     

Recent developments in the control of constrained hybrid systems

We review recently developed schemes for the constrained control of systems integrating logic and continuous dynamics. The control paradigm we focus on is model predictive control (MPC) and its derivatives, with the emphasis on explicit solution. The exposition of the basic theory is supplemented by a number of application case studies showing the effectiveness as well as the limitations of the deployed algorithms. Current and future lines of research are briefly discussed.     

Two observer-based nonlinear control approaches for temperature control of a class of continuous stirred tank reactors

In this paper, two nonlinear observer based controllers for temperature control of a continuous stirred tank reactor in which a special class of parallel exothermic reactions take place are proposed. A reduced order nonlinear observer is constructed to estimate the concentration in the reactor. The observer is coupled with two nonlinear controllers, designed based on two well-known techniques, namely input-output linearization and backstepping for controlling the reactor temperature. For dampening the effect of observer error dynamics, a compensating term is used in each control law. The asymptotical stability of the closed-loop system is shown by the Lyapunov's stability theorem. The effectiveness of the proposed controllers has been demonstrated through computer simulations.     

Observer-based nonlinear control law for a continuous stirred tank reactor with recycle

This paper proposes new results concerning the problem of the control of a continuous stirred tank reactor with recycle. The novelty of the proposed results consists of a new nonlinear observer-based controller which is found by means of recent results of differential geometry for time-delay nonlinear systems, without using linear approximations of the model. Local convergence of the system state to the arbitrarily chosen operating point is theoretically proved. The significance of the proposed control law is shown by many simulations, which show high performances with any initial conditions, even at the start-up, and with critical cases of mismatched parameter values.     

A variational approach to the control of electrochemical hydrogen reactions

Optimal control problems for the hydrogen evolution reaction (HER) system are solved for different cost objectives and admissible control strategies. The solution for the set-point-change process is given in analytical terms when the admissible controls are continuous functions of time. For piecewise-continuous controls the problem admits a solution as a feedback law. In both cases the cost functional penalizes the electrochemical power spent in the process, which translates into a Lagrangian more involved than classical quadratic. The theoretical framework remains into the Calculus of Variations for infinite-horizon problems. The Hamiltonian control formalism is adapted to treat the problem when the final time is bounded.     

Approximation of the optimal minimum-phase output for control of nonlinear nonminimum-phase processes

Nonminimum-phase parts are better removed in the feedback loop like the time delay term. For this, Wright and Kravaris [1992] proposed the concept of optimal minimum-phase output to control nonlinear nonminimumphase processes. However, their optimal minimum-phase output has no analytic solutions for processes with more than three state variables. Here, methods for analytic minimum-phase outputs approximating the optimal ones are proposed, having no limitations in the number of state variables. The proposed methods provide analytic solutions for processes with three state variables and simple numerical solutions for those with more state variables.     

Enhancement of laminar mixing by optimal control methods

Calculus of variations is applied to the problem of optimal mixing of two immiscible fluids in a laminar flow where stretching of the interface between the liquids is treated as an objective function. Under the action of an optimal control, the system behaves like that driven by a negative surface tension, i.e., an initially flat interface evolves in long tongues that are stretched and twisted by the flow. The method was tested for mixing by Stokes flow in lid-driven two-dimensional rectangular cavity.     

Nonlinear process monitoring using kernel principal component analysis

In this paper, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed. KPCA has emerged in recent years as a promising method for tackling nonlinear systems. KPCA can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions. The basic idea of KPCA is to first map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. In comparison to other nonlinear principal component analysis (PCA) techniques, KPCA requires only the solution of an eigenvalue problem and does not entail any nonlinear optimization. In addition, the number of principal components need not be specified prior to modeling. In this paper, a simple approach to calculating the squared prediction error (SPE) in the feature space is also suggested. Based on T² and SPE charts in the feature space, KPCA was applied to fault detection in two example systems: a simple multivariate process and the simulation benchmark of the biological wastewater treatment process. The proposed approach effectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA.     

Control of a solution copolymerization reactor using multi-model predictive control

We study the control of a solution copolymerization reactor using a model predictive control algorithm based on multiple piecewise linear models. The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function which has finite and infinite horizon cost components and uses multiple linear models in its predictions. The finite horizon cost consists of free input variables that direct the system towards a terminal region which contains the desired operating point. The infinite horizon cost has an upper bound and takes the system to the final operating point. Simulation results on an industrial scale methyl methacrylate vinyl acetate solution copolymerization reactor model demonstrate the ability of the algorithm to rapidly transition the process between different operating points.     

Considerations on nonlinear model predictive control techniques

The nonlinear model predictive control (NMPC) is an on-line application based on nonlinear convolution models. It is an appealing control methodology, but it is difficult to implement and its solution is not so performing since it unavoidably means to solve a usually large-scale, constrained, and multidimensional optimization. To increase the difficulty, this optimization problem is subject to computationally heavy differential and algebraic constraints constituting the same convolution model and the least squares nature of the objective function easily leads to narrow valleys and multimodality issues.Beyond a short review of the state-of-the-art, the paper is aimed at highlighting the possibility to exploit at best the intrinsic features of the specific system one is going to control using the NMPC. The idea is to give the NMPC the possibility to automatically select the best combination of algorithms (differential solvers and optimizers) in accordance with the specific problem to be solved. From this perspective, the NMPC could be easily extended to many scientific fields traditionally far from process systems and computer-aided process engineering and the user has not to worry about which specific differential solvers and optimizers are needed to solve his/her problem.     

Fuzzy model predictive control of nonlinear pH process

A new fuzzy model-based predictive control scheme was developed to control a nonlinear pH process. The control scheme is based on the Takagi-Sugeno type fuzzy model of the process being controlled. In the present fuzzy model predictive control method, the process model maintains a linear representation of the conclusion parts of fuzzy rules. Therefore, it has a significant advantage over other types of models in the sense that nonlinear processes can be handled effectively by embedding the linear characteristic. The fuzzy model of the pH process to be controlled was constructed and used in the predictive control algorithm. Results of computer simulations and experiments demonstrated the effectiveness of the present control method.     

Model based control of a liquid swelling constrained batch reactor subject to recipe uncertainties

This work presents the application of nonlinear model predictive control (NMPC) to a simulated industrial batch reactor subject to safety constraint due to reactor level swelling, which can occur with relatively fast dynamics. Uncertainties in the implementation of recipes in batch process operation are of significant industrial relevance. The paper describes a novel control-relevant formulation of the excessive liquid rise problem for a two-phase batch reactor subject to recipe uncertainties. The control simulations are carried out using a dedicated NMPC and optimization software toolbox OptCon which implements efficient numerical algorithms. The open-loop optimal control problem is computed using the multiple-shooting technique and the arising nonlinear programming problem is solved using a sequential quadratic programming (SQP) algorithm tailored for large-scale problems, based on the freeware optimization environment HQP. The fast response of the NMPC controller is guaranteed by the initial value embedding and real-time iteration technologies. It is concluded that the OptCon implementation allows small sampling times and the controller is able to maintain safe and optimal operation conditions, with good control performance despite significant uncertainties in the implementation of the batch recipe.     

Robust controller synthesis for multivariable nonlinear systems with unmeasured disturbances

This work concerns robust controller synthesis using the differential geometric concepts for minimum phase nonlinear systems with unmeasurable disturbances. A pseudo-linearization of the disturbance model at the input-output linearization stage is applied to yield a linear subsystem for controller design. Based on this linear model, a multi-loop controller framework is implemented, whereby μ-synthesis is used to design off-line robust controller in the outer loop while state feedback is implemented in the inner loop. Through proper selection of weights, the outer robust controller is explicitly designed to address both uncertainty and disturbance rejection whereas the inner controller is used for on-line static state feedback. Numerical simulations are used to illustrate robustness of the controller for multi-input multi-output temperature control in two non-isothermal continuous stirred tank reactors in series.     

Effect of the choice of final time in optimal control of nonlinear systems

The choice of the final time, t_j, in the optimal control of nonlinear systems is shown to be very important. By choosing t_f to be small, and repeatedly optimizing the system operation over the short time intervals gives a highly oscillatory type of control for a particular nonlinear chemical reactor. The cumulative profit as compared to that obtained by choosing t_f to be large, is substantially lower. In the operation of a batch reactor it is shown that if t_f is small, bang-bang control with singular sub-arcs results. When t_f is large, the optimal control policy tends to be relatively smooth and the profitability is substantially improved.     

Two-degree-of-freedom output feedback controllers for nonlinear processes

In this work, a nonlinear output feedback control algorithm is proposed, in the spirit of model-state feedback control. The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output. These estimates will track the process states at a rate determined by a set of tunable parameters. An algebraic transformation of the state estimates is incorporated in the control structure to ensure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The transformed states are then used in the control law. This leads to a controller of minimal order possessing integral action. The control structure is shown to have the same properties as the standard model-state feedback structure. The resulting algorithm is a two-degree of freedom control law, in the sense that the control action is not a function of the error only, but the output and the set point are processed in different ways. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to demonstrate the closed-loop performance of the proposed method.     

Time optimal control of high dimensional systems by iterative dynamic programming

For time optimal control, the problem is first transformed into a finite dimensional optimization problem by using time stages of varying lengths to enable accurate switching, and then solved by iterative dynamic programming. In high dimensional systems care is necessary to ensure convergence to the global optimum. Incorporating penalty functions into the performance index and using an adequate number of stages can yield the global optimum. The use of a continuation approach, where the number of stages in an intermediate solution is systematically increased, appears to be more effective. Three linear systems are used to develop and to test the approaches.     

State-preserving nonlinear model reduction procedure

Model reduction has proven to be a powerful tool to deal with challenges arising from large-scale models. This is also reflected in the large number of different reduction techniques that have been developed. Most of these methods focus on minimizing the approximation error; however, they usually result in a loss of physical interpretability of the reduced model. A new reduction technique, which preserves a non-prescribed subset of the original state variables in the reduced model, is presented in this work. The technique is derived from the Petrov–Galerkin projection by adding constraints on the projection matrix. This results in a combinatorial problem of which states need to be selected. A sequential algorithm has been developed based on the modified Gram–Schmidt orthogonalization procedure. The presented technique is applied to two examples where the reduction error is found to be comparable to the traditional POD method. At the same time, the technique has the advantage that the physical interpretation of the remaining states is retained.