A moving horizon approach to input design for closed loop identification

The identification of high fidelity models is a critical element in the implementation of high performance model predictive control (MPC) applications in the industry. These controllers can vary in size with input–ouput dimensions ranging from 5 × 10 to 50 × 100. Identifying models of this scale accurately is a time consuming and demanding exercise. We present a novel approach wherein an information rich test signal is generated in closed loop by maximizing the MPC objective, as opposed to minimization that is done in the standard controller. We show that the proposed input design approach is similar to T-optimal (trace optimal) experiment design method. Our approach automatically accounts for the input and output constraints and is implemented in a moving horizon manner. It is demonstrated through simulation examples on both well and ill-conditioned processes.     

Predictive functional control of an axis positioning system with an estimator-based internal model

This paper deals with an application of the predictive functional control with a state estimator-based internal model (PFC_ EBIM). The PFC_ EBIM has been shown to be effective in simulation. However, neither detailed experimental validation nor comparison with other controllers has been reported thus far. Here, the PFC_ EBIM is implemented in a single-axis positioning system, and a few experimental tests are conducted. Tracking performance of the PFC_ EBIM, standard PFC, and P – PI control for both smooth and non-smooth reference signals are compared. The experimental results prove the effectiveness of the PFC_ EBIM.     

Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process

This paper presents a two-level hierarchical control structure for biological wastewater treatment plants, with the goal of improving effluent quality and reducing operational costs. The Benchmark Simulation Model No. 1 is used as working scenario. The hierarchical structure allows to adjust the dissolved oxygen in the fifth tank (S_O,5) according with the working conditions, instead of keeping it in a fixed value. Model Predictive Control (MPC) with inlet flow rate feedforward control (MPC + FF) is proposed for the lower level to control nitrate nitrogen concentration of the second tank and S_O,5. MPC, Affine Function and fuzzy controller are tested for the higher level to adjust the S_O,5 set point of the lower level based on the ammonium and ammonia nitrogen concentration in the fifth tank. Modifying the tuning parameters of the higher level, a tuning region is determined, in which the effluent quality and operational costs are simultaneously improved.     

基于等价输入干扰滑模观测器的磁悬浮球系统模型预测控制

本文针对系统不确定性和外部干扰引起的磁悬浮球系统控制性能下降的问题,提出了一种基于等价输入干扰滑模观测器的模型预测控制(MPC+EIDSMO)方法.首先将原系统转化为EID系统,采用等价输入干扰滑模观测器对EID系统状态变量及等价输入干扰进行估计;然后基于状态估计值设计模型预测控制器,并将等价输入干扰估计值以前馈的方式...     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.     

Experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning

In this paper, we consider the problem of finding decentralized controllers for heavy-duty vehicle (HDV) platooning by establishing empiric results for a qualitative verification of a control design methodology. We present a linear quadratic control framework for the design of a high-level cooperative platooning controller suitable for modern HDVs. A nonlinear low-level dynamical model is utilized, where realistic response delays in certain modes of operation are considered. The controller performance is evaluated through numerical and experimental studies. It is concluded that the proposed controller behaves well in the sense that experiments show that it allows for short time headways to achieve fuel efficiency, without compromising safety. Simulation results indicate that the model mimics real life behavior. Experiment results show that the dynamic behavior of the platooning vehicles depends strongly on the gear switching logic, which is confirmed by the simulation model. Both simulation and experiment results show that the third vehicle never displays a bigger undershoot than its preceding vehicle. The spacing errors stay bounded within 6.8 m in the simulation results and 7.2 m in the experiment results for varying transient responses. Furthermore, a minimum spacing of −0.6 m and −1.9 m during braking is observed in simulations and experiments, respectively. The results indicate that HDV platooning can be conducted at close spacings with standardized sensors and control units that are already present on commercial HDVs today.     

Stable indirect adaptive interval type-2 fuzzy sliding-based control and synchronization of two different chaotic systems

An indirect approach to adaptive interval type-2 fuzzy sliding mode control is proposed for the stable synchronization of two different chaotic nonlinear systems with different initial conditions under the presence of uncertainties involving process noises and external disturbances. The indirect model-based approach to adaptation is promoted here as a more suitable strategy for the fast changes that occurs in chaotic systems. In other words, the usual direct adaptive strategies may be too slow to respond to the inherently fast changing dynamics of chaotic systems. Using Lyapunov analysis, the sliding mode approach illustrates the asymptotic convergence of synchronization error to zero as well as good robustness against external disturbances. The interval type-2 structure aims to remedy the undesirable chattering phenomenon that is common in most conventional sliding mode control applications. It also provides a more effective equivalent model in the indirect approach, which leads to improved handling of the chaotic variations and uncertainties. Two numerical pairs of chaotic systems, i.e. the Lorenz and Chen’s systems and the Rössler system and modified Chua’s circuit are considered. In particular, in comparison with its type-1 fuzzy counterpart, the control effort is reduced by an average of 26.25% and 17.4% for the synchronization of the two corresponding systems, respectively. Furthermore, the integral of squared error is also improved by an average of 27.2% and 25.33%. This is while convergence time is reduced to less than 0.5 s and 1.5 s.     

Advanced control with parameter estimation of batch transesterification reactor

The objective of this work is to enhance the economic performance of a batch transesterification reactor producing biodiesel by implementing advanced, model based control strategies. To achieve this goal, a dynamic model of the batch reactor system is first developed by considering reaction kinetics, mass balances and heat balances. The possible plant-model mismatch due to inaccurate or uncertain model parameter values can adversely affect model based control strategies. Therefore, an evolutionary algorithm to estimate the uncertain parameters is proposed. It is shown that the system is not observable with the available measurements, and hence a closed loop model predictive control cannot be implemented on a real system. Therefore, the productivity of the reactor is increased by first solving an open-loop optimal control problem. The objective function for this purpose optimizes the concentration of biodiesel, the batch time and the heating and cooling rates to the reactor. Subsequently, a closed-loop nonlinear model predictive control strategy is presented in order to take disturbances and model uncertainties into account. The controller, designed with a reduced model, tracks an offline determined set-point reactor temperature trajectory by manipulating the heating and cooling mass flows to the reactor. Several operational scenarios are simulated and the results are discussed in view of a real application. With the proposed optimization and control strategy and no parameter mismatch, a revenue of 2.76 $ min⁻¹ can be achieved from the batch reactor. Even with a minor parameter mismatch, the revenue is still 2.01 $ min⁻¹. While these values are comparable to those reported in the literature, this work also accounts for the cost of energy. Moreover, this approach results in a control strategy that can be implemented on a real system with limited online measurements.     

Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller

A non-linear model predictive controller (NMPC) was investigated as a route to delivering improved product quality, batch to batch reproducibility and significant cost reductions by providing a means for better controlling the bioreactor environment in a Chinese hamster ovary (CHO) mammalian cell fed-batch process.A nonlinear fundamental bioprocess model was developed to represent the CHO mammalian cell fed-batch bioprocess under study. This developed nonlinear model aided in the configuration and tuning of a NMPC through off-line simulation. The tuned NMPC was applied to a 15 L pilot-plant bioreactor for glucose concentration fixed set-point control. Traditionally, bioprocesses are characterized by long critical process parameter (CPP) measurement intervals (24 h). However, advances in PAT have helped increase CPP measurement frequency. An in situ Kaiser RXN2 Raman spectroscopy instrument was used to monitor the glucose concentration at 6 min intervals.Glucose concentration control of a bioreactor is not a trivial task due to high process variability, measurement noise and long measurement intervals. Nevertheless, NMPC proved successful in achieving closed loop fixed set-point control in the presence of these common bioprocess operation attributes.     

Computation delay compensation for real time implementation of robust model predictive control

The implementation of model predictive control (MPC) requires to solve an optimization problem online. The computation time, often not negligible especially for nonlinear MPC (NMPC), introduces a delay in the feedback loop. Moreover, it impedes fast sampling rate setting for the controller to react to uncertainties quickly. In this paper, a dual time scale control scheme is proposed for linear/nonlinear systems with external disturbances. A pre-compensator works at fast sampling rate to suppress uncertainty, while the outer MPC controller updates the open loop input sequence at a slower rate. The computation delay is explicitly considered and compensated in the MPC design. Four robust MPC algorithms for linear/nonlinear systems in the literature are adopted and tailored for the proposed control scheme. The recursive feasibility and stability are rigorously analysed. Three simulation examples are provided to validate the proposed approaches.     

Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger

In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for PID + BELBIC.     

A multiple model approach for predictive control of nonlinear hybrid systems

This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.     

A habituating blood glucose control strategy for the critically ill

In the intensive care unit patients benefit from being fed and from having well controlled glucose levels. Insulin and glucose infusion serves as manipulated inputs to regulate blood glucose, while glucose infusion serves as a sole nutritional input. In this paper, a model predictive control strategy, based on simultaneously manipulating glucose and insulin infusion, is developed to improve blood glucose regulation in intensive care unit patients. In the short term, glucose infusion is used for tighter glucose control, particularly for disturbance rejection, while, in the long-term (24 h period), glucose infusion is used to meet nutritional needs. The “habituating control” algorithm is proposed and tested against a model predictive control (MPC) strategy that only manipulates insulin. The simulation results indicate that the Habituating MPC strategy outperforms the single input–single output MPC by providing faster setpoint tracking and tighter glucose control for a patient population, and producing less glucose variability while rejecting disturbances in insulin infusion and insulin sensitivity.     

基于多模糊模型的非线性预测控制

研究了基于多模糊模型的非线性预测控制问题 ,提出了基于多模型融合的非线性预测控制方法 .首先根据实际对象在不同运行点附近的状态建立了非线性系统的线性多模糊模型表示 ,然后给出了基于多模糊模型的预测控制原理结构框图 .非线性多模糊模型被用来作为预测模型 ,CSTR过程的仿真研究表明是一种有前景的非线性预测控制方法 .     

Feedback PID-like fuzzy controller for pH regulatory control near the equivalence point

In this research the use of a feedback PID-like fuzzy controller scheme for pH control is presented to deal with instability problems near the equivalence point in neutralization processes. State space analysis of the titration curves and a fuzzy clustering algorithm based on calculating a measure of potential derived from the square distance of the pH data are complementary applied to define the membership structure and the fuzzy sets of the controller. To test the performance of the controller, both simulated and experimental runs were used. The fuzzy controller was tested for compensating step-change perturbations of propionic acidic flow rates, propionic acid concentration, and buffering conditions. Stationary cycling behavior has been observed for large loads of acidic flow rates. It was found that though the rejection time was strongly dependent on the mean residence time of the liquid solutions, the proposed controller keep the neutralization process operating close to the specified set point of pH = 7.     

Design and experimental implementation of an effective control system for thin film Cu(InGa)Se2 production via rapid thermal processing

Copper indium gallium diselenide, Cu(InGa)Se₂ (CIGS) solar cells have achieved efficiencies of 22.3% at the cell level and 17.5% at the module level. CIGS-based modules are also in the early stages of commercialization, with >1 GW annual production capacity. The most common method for producing CIGS in industry is via precursor reaction, which consists of depositing Cu-In-Ga precursor films and reacting them with gas-phase H₂Se at 450–550 °C for 60 min or longer, and is commonly called selenization. Recently, interest has been growing in selenization by Rapid Thermal Processing (RTP), which is characterized by rapid temperature ramping, approximately 550 °C or higher temperature reactions, and improved process throughput. However, it has been difficult to commercialize RTP for CIGS film production in part because implementing a rapid, linear temperature ramp in the reacting thin film is complicated by two intrinsic process characteristics: (i) the temperature of the CIGS film cannot usually be measured directly; and (ii), the process is significantly nonlinear due to the dominance of radiative heat transfer at high temperatures. In this paper, we present the design and modeling, construction, and successful operation of a pilot-scale RTP selenization reactor utilizing a novel temperature control system.Our two-fold approach to the unique temperature control challenges involves the design and implementation of (i) a first-principles, model-based observer to estimate the desired surface temperature; and (ii) a specialized controller to enable effective tracking of the desired linear temperature ramp set point. Our experimental results demonstrate that the control system is effective in tracking rapid temperature ramps accurately, with performance limited only by the physical constraints of the experimental system.     

Vibration control of a Stirling engine with an electromagnetic active tuned mass damper

Active tuned mass damper (ATMD) systems have been used extensively to reduce vibrations in machines. The motivation of this study is attenuating the vibrations in a Free-Piston Stirling Engine/Linear Alternator (FPSE/LA) for a frequency band of 47–53 Hz using an electromagnetic ATMD that employs a linear Voice Coil Motor (VCM) for periodic excitation rejection. To the authors’ knowledge, however several approaches to minimize vibrations in Stirling machines have been patented, the technique proposed in this research differs from other patented work by the simplicity of the proposed control law which aims to attenuate the engine vibrations at the fundamental operating frequency. The proposed control system features a zero-placement technique that utilizes both relative or absolute position and velocity feedback from the system response as well as a feedthrough measurement of the disturbance frequency that is used to determine the position gain online. The performance of the control system with the ATMD was evaluated both theoretically and experimentally. A test rig emulating the vibration behavior of the Stirling engine, featuring an electrodynamic shaker and an ATMD was developed and a model of the rig is presented and validated. A novel experimental procedure of identifying unknown stiffness and unknown dynamic mass of a spring–mass system is also presented. Similarly, another experimental procedure of determining the damping coefficient in the electromagnetic ATMD is shown. The implementation findings illustrate that the proposed active controller succeeds in broadening the attenuation band from 50±0.5 Hz to between 45 Hz and 55 Hz.     

Estimation and adaptive nonlinear model predictive control of selective catalytic reduction systems in automotive applications

The demands of high NO_x conversion efficiency and low tailpipe ammonia slip for urea-based selective catalytic reduction (SCR) systems have been substantially increased in the past decade, as NO_x emission legislations for Diesel engines are becoming more stringent than ever before. Since catalyst aging has a significant impact on SCR performance, robust and adaptive SCR control has been preferred for degraded SCR systems to realize emission control objectives. The purpose of this paper is twofold. Firstly, a robust ammonia coverage ratio observer was designed for estimating the ammonia coverage ratio reference for catalysts with different aging levels. An ammonia storage capacity observer was developed for estimating the actual ammonia storage capacity which can be reduced due to catalyst aging. An adaptive ammonia coverage ratio reference design was then developed to estimate the desired ammonia coverage ratio ranges at each instantaneous engine operating point for both single-cell and two-cell SCR systems at different aging levels based on a singular perturbation method. Secondly, to ensure the estimated ammonia coverage ratio falls in the desired ranges for most of engine operating conditions, robust nonlinear model predictive control (NMPC) algorithms were designed for both single-cell and two-cell SCR systems. Experimental data over US06 cycle were collected from a Diesel engine and aftertreatment system platform for controller verification. Simulation results under US06 test cycle demonstrate that the proposed NMPC algorithms were capable of consistently achieving high NO_x conversion efficiency (>95.6%) and constrained tailpipe ammonia slip (<10 ppm on average and <12 ppm on the peak) for both fresh catalyst and aged catalyst with 30% loss of ammonia storage capacity.