Decentralized robust PI controller design for an industrial boiler

This paper presents a new scheme to design decentralized robust PI controllers for uncertain LTI multivariable systems. Sufficient conditions for closed-loop stability and closed-loop diagonal dominance (almost decoupling) of a multivariable system are obtained. Satisfying these conditions and robust performance of the overall system are modeled as local robust performance problems. Then, by appropriately selecting the time constants of the closed-loop isolated subsystems in the IMC (Internal Model Control) strategy, the defined local robust performance problems are solved. To design a decentralized robust PI controller for a real industrial utility boiler, a control oriented nonlinear model for the boiler is identified. The nonlinearity of the system is modeled as uncertainty for a nominal LTI multivariable system. Using the new proposed method, a decentralized PI controller for the uncertain LTI model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.     

Robust stabilization of nonlinear interconnected systems with application to an industrial utility boiler

This paper presents a new scheme for robust stabilization of nonlinear interconnected systems, based on linear matrix inequalities (LMIs). The fact that the improvement in stability is significant and the controller uses only the output information of plant leads to the name robust output feedback control. The control design is formulated as a convex optimization problem, which makes it computationally tractable, when the problem size increases. The controller concept is then evaluated on a natural circulation drum boiler (utility boiler), where the nonlinear model describes the complicated dynamics of the drum, downcomer, and riser components. The linearized system is non-minimum phase and has two poles at the origin, which are major sources of interaction, bandwidth limitation and instability. Simulation results are presented which show the effectiveness of the proposed control against instabilities following sudden load variations. The control is also effective for steady state operation.     

Fuzzy modeling and stable model predictive tracking control of large-scale power plants

This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi–Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler–turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation.     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

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.     

Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MW_th fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.     

Robust decentralized direct adaptive output feedback fuzzy control for a class of large-sale nonaffine nonlinear systems

In the previous work of Huang et al., a decentralized direct adaptive fuzzy H_∞ tracking controller of large-scale nonaffine nonlinear systems is obtained predicated upon the assumption that the mismatching error dynamics stay squared integrable. In this note, we focus in the absence of the conservative assumption upon developing a robust decentralized direct adaptive output feedback fuzzy controller. By combination of a state observer, a fuzzy inference system and robust control technique, the previous controller design is modified and no a priori knowledge of bounds on lumped uncertainties is required. All the signals of the closed-loop large-scale system are proved to be uniformly ultimately bounded. The effectiveness of the developed scheme is demonstrated through the simulation results of interconnected inverted pendulums.     

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.     

CBA-neural network control of a non-linear full vehicle model

In this paper, the dynamic behavior of a non-linear eight degrees of freedom vehicle model having active suspensions and passenger seat controlled by a neural network (NN) controller is examined. A robust NN structure is established by using principle design data from the Matlab diagrams of system functions. In the NN structure, Classic Back-Propagation Algorithm (CBA) is employed. The user inputs a set of x₁ − x₁₆ while the output from the NN consists of f₁ − f₁₆ non-linear functions. Further, the Permanent Magnet Synchronous Motor (PMSM) controller is also determined using the same NN structure. According to various tests of the NN structure it is demonstrated that the model is able to give highly sensitive outputs for vibration condition, even using a more restricted input data set. The non-linearity occurs due to dry friction on the dampers. The vehicle body and the passenger seat using PMSM are fully controlled at the same time. The time responses of the non-linear vehicle model due to road disturbance and the frequency responses are obtained. Finally, uncontrolled and controlled cases are compared. It is seen that seat vibrations of a non-linear full vehicle model are controlled by NN based system exactly.     

H∞ decentralized fuzzy model reference tracking controldesign for nonlinear interconnected systems

In general, due to the interactions among subsystems, it is difficult to design an H_∞ decentralized controller for nonlinear interconnected systems. The model reference tracking control problem of nonlinear interconnected systems is studied via H_∞ decentralized fuzzy control method. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. A state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H∞ model reference tracking performance is achieved. Furthermore, the stability of the nonlinear interconnected systems is also guaranteed. If states are not all available, a decentralized fuzzy observer is proposed to estimate the states of each subsystem for decentralized control. Consequently, a fuzzy observer-based state feedback decentralized fuzzy controller is proposed to solve the H_∞ tracking control design problem for nonlinear interconnected systems. The problem of H _∞ decentralized fuzzy tracking control design for nonlinear interconnected systems is characterized in terms of solving an eigenvalue problem (EVP). The EVP can be solved very efficiently using convex optimization techniques. Finally, simulation examples are given to illustrate the tracking performance of the proposed methods     

一类线性不确定组合系统的输出反馈分散输出跟踪控制

讨论了线性不确定组合系统的鲁棒分散输出渐近跟踪问题.基于Lyapunov方程正 定解的存在性,给出了输出反馈跟踪控制器的设计方法.对于系统中所有允许的不确定性,所 设计的控制器均使系统的输出渐近跟踪所给定的参考信号,同时系统的状态保持有界.     

Weyl Closure of a Linear Differential Operator

We study the Weyl closure Cl(L)  = K(x)〈∂〉L ∩ D for an operator L of the first Weyl algebra D = K〈x, ∂〉. We give an algorithm to compute Cl(L) and we describe its initial ideal under the order filtration. Our main application is an algorithm for constructing a Jordan–Hölder series for a holonomic D -module and a formula for its length. Using the closure, we also reproduce a result ofStrömbeck (1978), who described the initial ideals of left ideals of D under the order filtration, and a result ofCannings and Holland (1994), who described the isomorphism classes of right ideals of D.     

A neural network theory for constrained optimization

A variety of real-world problems can be formulated into continuous optimization problems with constraint equalities. The real-world problem here can include, for example, the traveling salesman problem, the Dido’s isoperimetric problem, the Hitchcock’s transportation problem, the network flow problem and the associative memory problem. In spite of the significance, there has not yet been developed any robust solving method that works efficiently across a broad spectrum of optimization problems. The recent Hopfield’s neural network method to solve the traveling salesman problem is a potentially promising candidate because of the efficiency due to its parallel processing. His method, however, has certain drawbacks that must be removed away before it can be qualified for an efficient, robust solving method. That is: (a) locally minimum solutions instead of globally minimum; (b) possible infeasible solutions; (c) heuristic choice of network parameters and an initial state; (d) quadratic objective functions instead of arbitrary nonlinear objective functions with arbitrary nonlinear equality constraints; and (e) unorganized mathematical formulation of the network for extension. This paper develops from the Hopfield method an efficient, robust network solving method of the continuous optimization problem with constraint equalities that resolves all the drawbacks except for (a) that has already been resolved by others. The development is mathematically rigorous and thus constitutes a solid foundation of a neural network theory for constrained optimization.     

An analysis,design and tuning of Cascade Control Systems in the presence of constraints in actuator and process outputs

In the presence of rate constraints in actuator, design of cascade controller based on the primary controller conditional integration can result into closed-loop system oscillations. Stability analysis, performed by the describing function technique and confirmed by simulation, demonstrates that the solution based on the Anti-Reset Windup Cascade Control System (ARW CCS) structure is successful. Design and tuning of the ARW CCS secondary controller is a standard ARW single-input single-output problem. In the present paper tuning is proposed for the ARW CCS primary controller. For the serial process modeling simple rules are derived and confirmed by experimental results, obtained on a drum type boiler of a 210 MWe lignite coal fired unit. General design of the ARW CCS is based on the parallel process modeling and optimization of the primary controller. Optimization is performed in the frequency domain, under constraints on the maximum sensitivity, multiplicative uncertainty bound and sensitivity to measurement noise. Simulation and experimental results on a laboratory thermal plant demonstrate effectiveness of the proposed optimization.     

参数不确定机器人分散鲁棒跟踪控制

提出了一种新的参数不确定机器人分散控制器设计方法．首先将关节子系统的动力学模型分解为人工标称模型和非线性时变不确定模型两部分；然后分别设计相应的标称控制器和鲁棒补偿器．标称控制器使得标称闭环系统具有理想的跟踪性能；鲁棒补偿器可以抑制参数不确定和关节间非线性耦合等因素的影响，实现鲁棒跟踪．所设计的控制器只需要局部关节的位置反馈，具有易于实现和可在线调整的优点．仿真结果说明了该方法的有效性．     

Design of a Robust Optimal Decentralized PI Controller Based on Nonlinear Constraint Optimization For Level Regulation: An Experimental Study

This paper presents the development of a new robust optimal decentralized PI controller based on nonlinear optimization for liquid level control in a coupled tank system. The proposed controller maximizes the closed-loop bandwidth for specified gain and phase margins, with constraints on the overshoot ratio to achieve both closed-loop performance and robustness. In the proposed work, a frequency response fitting model reduction technique is initially employed to obtain a first order plus dead time (FOPDT) model of each higher order subsystem. Furthermore, based on the reduced order model, a proposed controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed optimal robust decentralized control scheme has been compared with that of a decentralized PI controller. The proposed controller is implemented in real-time on a coupled tank system. From the obtained results, it is shown that the proposed optimal decentralized PI controller exhibits superior control performance to maintain the desired level, for both the nominal as well as the perturbed case as compared to a decentralized PI controller.      

Active noise control in a forced-air cooling system

This paper discusses the design methodology for the active noise control of sound disturbances in a forced-air cooling system. The active sound cancellation algorithm uses the framework of output-error based optimization of a linearly parametrized filter for feedforward sound compensation to select microphone location and demonstrate the effectiveness of active noise cancellation in a small portable data projector. Successful implementation of the feedforward based active noise controller on a NEC LT170 data projector shows a 20–40 dB reduction per frequency point in the spectrum of external noise of the forced-air cooling system can be obtained over a broad frequency range from 1 to 5 kHz. A total noise reduction (unweighted) of 9.3 dB is achieved.     

Dynamic workforce allocation in a constrained flow shop with multi-agent system

The present work addresses the problem of real time workforce scheduling in assembly lines where the number of operators is less to the number of workstations.The problem is faced developing a two-steps procedure made of (i) a centralized scheduling based on a constraint optimization problem (COP) for initial operator scheduling, and (ii) a decentralized algorithm performed by a multiagent system (MAS) to manage workers in case of unforeseen events.In the proposed MAS architecture, Agents represent the operators trying to find local assignments for themselves. The system is validated with a simulation model and implemented with a hardware infrastructure in a real assembly line of electromechanical components. The main original contribution of the paper consists in proving – by means of both validation through a simulation model and test in a real assembly line of electromechanical components – that (1) multi-agent systems could be successfully adopted to solve a workforce scheduling problem, and (2) a combined approach consisting of centralized + distributed approach would provide better results compared with the application of one of the two approaches alone.     

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.     

Computer-based analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid-flow in rough-pipes. In this paper, the state-of-the-art review for the most currently available explicit alternatives to the Colebrook–White equation, is presented. An extensive comparison test was established on the 20 × 500 grid, for a wide range of relative roughness (ε/D) and Reynolds number (R) values (1 × 10^?6 ? ε/D ? 5 × 10^?2; 4 × 10³ ? R ? 10⁸), covering a large portion of turbulent flow zone in Moody’s diagram. Based on the comprehensive error analysis, the magnitude points in which the maximum absolute and the maximum relative error are occurred at the pair of ε/D and R values, are observed. A limiting case of the most of these approximations provided friction factor estimates that are characterized by a mean absolute error of 5 × 10^?4, a maximum absolute error of 4 × 10^?3 whereas, a mean relative error of 1.3% and a maximum relative error of 5.8%, over the entire range of ε/D and R values, respectively. For practical purposes, the complete results for the maximum and the mean relative errors versus the 20 sets of ε/D value, are also indicated in two comparative figures. The examination results for error properties of these approximations gives one an opportunity to practically evaluate the most accurate formula among of all the previous explicit models; and showing in this way its great flexibility for estimating turbulent flow friction factor. Comparative analysis for the mean relative error profile revealed, the classification for the best-fitted six equations examined was in a good agreement with those of the best model selection criterion claimed in the recent literature, for all performed simulations.