Robust continuous velocity control of convective spatially distributed systems

In this work a robust nonlinear scheme is proposed to control spatially distributed convective systems described by first-order hyperbolic partial differential equations by manipulating the flow velocity. The proposed scheme is designed after the method of characteristics is used to establish key structural properties of the system dynamics. The resulting feedback control, which can be seen as a proportional integral controller with variable integration time, does not require measurements for several axial points nor infinite dimensional state estimations. The proposed controller is applied successfully to two heat exchange simulation examples and a nonisothermical plug flow reactor. It is shown that it is robust in the face of uncertain parameters and load disturbances. Finally, the performance of the robust controller is compared to other control applications.     

A simple adaptive control strategy for temperature trajectory tracking in batch processes

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.     

A MODEL-BASED DIRECT ADAPTIVE PI CONTROLLER FOR NONLINEAR PROCESS CONTROL

This article proposes a model-based direct adaptive proportional-integral (PI) controller for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed direct adaptive PI controller is composed of two parts: the first is a linearizing feedback control law that is synthesized directly based on the process's nominal model and the second is an adaptive PI controller used to compensate for the model errors. An effective parameter-tuning algorithm is devised such that the proposed direct adaptive PI controller is able to achieve stable and robust control performance under uncertainties. To show the robust stability and performance of the direct adaptive PI control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed PI control strategy are demonstrated by considering the time-dependent temperature trajectory tracking control of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances, and measurement noises. Furthermore, for use in an environment that lacks full-state measurements, the integration of a sliding observer with the proposed control scheme is suggested and investigated. Extensive simulation results reveal that the proposed model-based direct adaptive PI control strategy enables a highly nonlinear process to achieve robust control performance despite the existence of plant/model mismatch and diversified process uncertainties.     

ROBUST CONTROL OF BATCH REACTORS

The design of robust nonlinear feedback controller is analysed for a trajectory tracking in a single-input single-output nonlinear state variable system x = f(x) + g(x)u, y=cx which arises in nonlinear chemical processes particularly in batch reactor control problems. Simulation results for the batch reactor temperature tracking problem show the effectiveness of the control scheme and its robustness to modelling errors. The method is also applicable to multi-input multi-output system where the number of inputs is equal to that of outputs. The controller design is also analyzed for situations wrier: the kinetics, the activation energy and Ihe heat of reaction are unknown and also only limited measurement of state-variables are available. The method of Youcef-Toumi and Ito (1987) is applied to such problems and the effectiveness of control system is shown by simulation.     

Model based robust control approach for batch crystallization product design

The paper presents a novel control approach for crystallization processes, which can be used for designing the shape of the crystal size distribution to robustly achieve desired product properties. The approach is based on a robust optimal control scheme, which takes parametric uncertainties into account to provide decreased batch-to-batch variability of the shape of the crystal size distribution. Both open-loop and closed-loop robust control schemes are evaluated. The open-loop approach is based on a robust end-point nonlinear model predictive control (NMPC) scheme which is implemented in a hierarchical structure. On the lower level a supersaturation control approach is used that drives the system in the phase diagram according to a concentration versus temperature trajectory. On the higher level a robust model-based optimization algorithm adapts the setpoint of the supersaturation controller to counteract the effects of changing operating conditions. The process is modelled using the population balance equation (PBE), which is solved using a novel efficient approach that combines the quadrature method of moment (QMOM) and method of characteristics (MOC). The proposed robust model based control approach is corroborated for the case of various desired shapes of the target distribution.     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

ABSTRACT

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

Performance of control strategies to track the trajectories of batch reactors

We propose a modified globally linearizing control (MGLC) structure and a nonlinear feedforward-feedback control (NFF/FB) structure to track trajectories of processes in a batch reactor. The MGLC structure performs trajectory tracking perfectly when the model inversion can be obtained by linearization of state feedback. Otherwise, the NFF/FB structure is recommended. The performance of the control laws that we developed are compared with other control laws designed with the same technique. The proposed control law based on the MGLC structure exhibits robust performance whereas that based on NFF/FB structure produces decreased sensitivity to process noise.     

自适应鲁棒迭代学习控制在陶瓷机械手中的应用

针对一类不确定非线性系统,结合自适应鲁棒控制和迭代学习控制方法,提出了自适应鲁棒迭代学习混合控制策略。学习控制策略用于处理周期性不确定,自适应鲁棒控制策略用于处理具有未知上界的非周期性不确定。所提出的控制方案保证跟踪误差在有限的迭代步骤内收敛到任意指定的误差区域。最后将此控制策略应用于陶瓷机械手的控制,仿真结果表明此方法的有效性。     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

Multi‐controller scheme for load rejection and set‐point tracking

To acheive complete compensation for loads, a novel multi‐controller scheme with feedforward control is proposed. This scheme has four controllers, a set‐point controller, two load controllers, and a feedforward controller. This results in the separation of the load response from the set‐point response in a closed‐loop system. These four controllers can then be designed independently to achieve good system performance for both set‐point tracking and load rejection. One of the load controllers can be chosen as a proportional controller; this guarantees physical realizability and provides excellent compensation. The results of simulation and real time control show that the proposed multi‐controller scheme is superior to a double‐controller system and a Smith predictor in the presence of large uncertainty in process dynamics especially for load disturbances.     

积分和不稳定时滞对象的改进内模控制

针对化工过程中一阶积分和不稳定时滞对象,基于内模控制提出了两自由度控制方案。首先根据鲁棒控制理论H2最优性能指标设计设定值跟踪控制器,然后采用期望闭环补灵敏度函数确定扰动抑制控制器。设定值跟踪控制器和扰动抑制控制器可通过性能参数独立调节而无需再取折衷,同时保证系统具有较好的鲁棒稳定性。最后通过仿真实例验证了该控制方案的有效性。     

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller （ILRGCC）. A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities （LMIs）, and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.     

Improved infinite horizon LQ tracking control for injection molding process against partial actuator failures

Focusing on injection molding processes with partial actuator failures, a new design of infinite horizon linear quadratic control is introduced. A new state space process model is first derived through input–output process data. Furthermore, an improved infinite horizon linear quadratic control scheme, whereby the process state variables and tracking error can both be regulated separately, is proposed to show enhanced control performance against partial actuator failures and unknown disturbances. Under the circumstances of actuator faults, the closed-loop system is indeed a process with uncertain parameters. Hence, a sufficient condition is proposed to guarantee robust stability is presented using Lyapunov theory. The proposed concepts are illustrated in an injection velocity control case study to show the effectiveness.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.     

Nonlinear predictive control using multi-rate sampling

A nonlinear predictive control (NLPC) strategy based on a nonlinear, lumped parameter model of the process is developed in this paper. A constrained optimization approach is used to estimate unmeasured state variables and load disturbances. Additional model/process mismatch is handled by using an additive output term which is equivalent to the Internal Model Control approach. Similar to linear predictive control methods, an optimal sequence of future control moves is determined in order to minimize an objective function based on a desired output trajectory, subject to manipulated variable constraints (absolute and velocity). Deadtime is explicitly included in the model formulation, giving NLPC the same deadtime compensation feature of linear model-predictive techniques. The multi-rate sampling nature of most chemical processes is also used to improve estimates of process disturbances. Infrequent composition measurements in conjunction with frequent temperature measurements are used to improve the “inferential” control of the composition in a continuous flow stirred tank reactor (CSTR).     

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

This article mainly focuses on disturbance rejection of dead-time processes by integrating a modified disturbance observer (MDOB) with a model predictive controller (MPC). The effect caused by model mismatches is regarded as a part of the lumped disturbances. This means that the disturbances considered here include not only external disturbances, but also internal disturbances caused by model mismatches. Control structure of the proposed method includes two parts which can be designed separately. The MPC which acts as a prefilter, is employed to generate appropriate control actions such that a desired setpoint tracking response is achieved. The MDOB is employed to estimate the disturbances of the closed-loop system, and the estimation is used for feedforward compensation design to reject disturbances. Rigorous analysis of setpoint tracking and disturbance rejection properties of the closed-loop system are given in the presence of both model mismatches and external disturbances. The proposed scheme is applied to control the temperature of a simplified jacketed stirred tank heater (JSTH). Simulation results demonstrate that the proposed method possesses a better disturbance rejection performance than those of the MDOB-PI, MPC and PI methods in controlling such dead-time processes.     

Neural network‐based optimal iterative controller for nonlinear processes

A new optimal iterative neural network‐based control (OINNC) strategy with simple computation and fast convergence is proposed for the control of processes with nonlinear dynamics. The process dynamics is captured by a forward neural network, and the control is determined by a simple iterative optimization during each sampling interval based on a linearized neural network model. In addition, a feedback control is incorporated into the system to compensate for any model mismatches and to reject disturbances. With the proposed system, the tracking error is shown to be confined to the origin. An application of the proposed OINNC scheme to a nonlinear process results in superior performance when compared with a well‐tuned conventional PID controller.     

Passivity-based control of the phthalic anhydride fixed-bed reactor

The control of tubular fixed-bed reactors with highly exothermic reactions is approached from a passivity-based control perspective. The proposed controller solves dynamic tracking of the reactor exit conversion and stabilization of the reactor temperature by exploiting the passivity properties of the process. The model-based control structure proposed in this paper provides a suitable framework for developing the passivity-based control law and the state predictor. The integrated controller is designed and its performance in the face of parameter variation and model uncertainty is tested by numerical simulation. Digital simulation on an industrial phthalic anhydride fixed-bed reactor shows that the proposed control scheme can give satisfactory dynamic tracking ability and disturbance rejection performance, which is robust in the presence of process variation and model uncertainty. This paper provides a basic insight into the characterization and solution of control problems that are particular to tubular fixed-bed reactor systems and constitutes the application of passivity-based control theory to complex chemical processes.