Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller.     

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.     

Fuzzy attitude control for a nanosatellite in low Earth orbit

In order to develop and introduce intelligent systems in the space field, an adaptive fuzzy logic controller is designed for a nanosatellite. Attitude determination and control subsystem (ADCS) and its performance and efficiency are compared with a traditional proportional integrative derivative (PID) controller. Fuzzy controllers have already been studied for satellite attitude control; however their performance has not been compared with the classical PID controllers typically being implemented on board spacecrafts currently. Both controllers have been designed and implemented in order to be tested on board a nanosatellite (QBITO) in a nearby mission (QB50), a constellation of 50 nanosatellites. Due to the requirements imposed by the mission, the orbit, and the significant limitations in the power available in these small spacecrafts, an efficient ADCS is required in order to fulfill the mission objectives. The comparison between the classical PID and the fuzzy controllers shows that the fuzzy controller is much more efficient in single maneuver (up to 65% less power required), achieving better precision in general than the PID. This shows that the use of this type of intelligent control systems is a great advantage over conventional control systems currently being used in satellite attitude control, and open new possibilities of application of intelligent controllers in the field of space technologies.     

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.     

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.     

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.     

Simultaneous dissolved oxygen and glucose regulation in fed-batch methionine production using decoupled input–output linearizing control

Methionine is one of the essential amino acids produced by fed-batch fermentation. The synthesis of methionine at the cellular level is strictly regulated and its process dynamics shows a nonlinear interaction between dissolved oxygen and glucose concentration. For controlling this process, a decoupled input–output linearizing controller (DIOLC) is derived. The model used for this purpose contains an exponential kinetic structure for describing the nonlinearities and metabolic switching function for describing oxygen dependency. The control system is square having two inputs and two outputs. The zero dynamics stability for internal variables and error convergence is proved. The performance of DIOLC is examined under high and low oxygen demand conditions. Four case studies are used to demonstrate that the DIOLC action is decoupled. The DIOLC also exhibited robust performance even for random variations up to ±20% in some parameters. In simulated experiments, using DIOLC produces 16 g l^?1 of methionine. The performance of PI controllers under identical conditions is given for comparison.     

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.     

Computational intelligence approaches and linear models in case studies of forecasting exchange rates

Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional auto regressive moving average (ARMA) and ARMA generalized auto regressive conditional heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min, 60 min and 120 min, daily and weekly basis, the one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series’ frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models.     

Generation of an optimal architecture of neuro force controllers for robot manipulators in unknown environments using genetic programming with fuzzy fitness evaluation

 In this paper, we have applied genetic programming to generate an optimal architecture of neuro force controllers for robot manipulators in any environment. In order to perform precise force control in unknown environments, the optimal structured neuro force controller is generated using genetic programming with fuzzy fitness evaluation. After the architecture of the neuro controller has been optimized for any kinds of environments, it can be applied for a robot contact task with an unknown environment in on-line manner using its own adaptation ability. An effective crossover operation is proposed for the efficient evolution of the controllers. The simulation has been carried out to evaluate the effectiveness of the proposed robot force controller.     

Adaptive control using neural networks and approximate models

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.     

On active acceleration control of vibration isolation systems

Active vibration isolation systems (VIS) have been widely used from the space shuttle applications to the ground vehicle suspensions. The main control objective is to achieve the minimum vibrations at the flotor for given vibrations at the stator. With respect to a fundamental limitation of using the PD type flotor acceleration controller, an I (integral) and II (double integral) type flotor acceleration controller is proposed in this paper. By incorporating the feedforward compensation of the umbilical dynamics, the proposed acceleration controller is able to experimentally push down the lowest isolation frequency from 1.4 Hz (when PID control is used) to 0.03 Hz with a sufficiently improved vibration isolation performance up to 10 Hz, with respect to a MIM (Microgravity Vibration Isolation Mount) system tested on the ground. A unique frequency selective filter (FSF) is also proposed, which experimentally suppresses a fixed-frequency umbilical resonant mode at 22.2 Hz with an attenuation of 20 dB.     

Design of artificial neuron controller for STATCOM in dSPACE environment

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt–ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.     

Selftuning control of some pilot plant processes

This paper reviews the results of some experimental tests carried out to evaluate the performance of selftuning (ST) controllers for temperature control of a continuous stirred tank heater, composition control of a binary distillation column and pH control of an acidic effluent. All the pilot plant units have been controlled using a single variant selftuning control, and a newly developed multivariant ST controller was used for simultaneous control of the terminal compositions of the distillation column. The control performance of the units operating under ST control is compared to that obtained using very well tuned proportional plus integral (PI) or proportional plus integral plus derivative (PID) conventional controllers. Control of pH is shown using a technique of electrochemical neutralisation coupled with a single variant ST controller. The control algorithms have been programmed on a number of microprocessor- and minicomputer-based systems. Z80 for stirred tank heater control, LS1-11 for pH control and HP1000 distributed computer system for distillation control.     

神经元控制器在直流调速系统中的仿真研究

该文提出了两种单神经元控制器,即数字神经元控制器和模拟神经元控制器,分别将这两种神经元控制器应用于直流双闭环调速系统,并进行了仿真研究,仿真工具采用了SIMULINK软件包,仿真结果表明这两种控制器都具有自适应的功能．均可应用于直流调速系统中,并获得满意的控制效果,但模拟神经元控制器具有更好的快速性和较小的超调。     

Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement

Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted.     

Non‐smooth structured control design with application to PID loop‐shaping of a process

Feedback controllers with specific structure arise frequently in applications because they are easily apprehended by design engineers and facilitate on‐board implementations and re‐tuning. This work is dedicated to H_∞ synthesis with structured controllers. In this context, straightforward application of traditional synthesis techniques fails, which explains why only a few ad hoc methods have been developed over the years. In response, we propose a more systematic way to design H_∞ optimal controllers with fixed structure using local optimization techniques. Our approach addresses in principle all those controller structures which can be built into mathematical programming constraints. We apply non‐smooth optimization techniques to compute locally optimal solutions, and provide practical tests for descent and optimality. In the experimental part we apply our technique to H_∞ loop‐shaping proportional integral derivative (PID) controllers for MIMO systems and demonstrate its use for PID control of a chemical process. Copyright © 2007 John Wiley & Sons, Ltd.     

Multi-model adaptive control of a simulated pH neutralization process

A multi-model adaptive PID controller is developed and evaluated in a simulation study for a nonlinear pH neutralization process. The performance and robustness characteristics of the multi-model controller are compared to those for conventional PID controllers and an alternative “multi-model interpolation” controller.     

Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming

Compressive strength and splitting tensile strength are both mechanical properties of concrete that are utilized in structural design. This study presents gene expression programming (GEP) as a new tool for the formulations of splitting tensile strength from compressive strength of concrete. For purpose of building the GEP-based formulations, 536 experimental data have been gathered from existing literature. The GEP-based formulations are developed for splitting tensile strength of concrete as a function of age of specimen and cylinder compressive strength. In experimental parts of this study, cylindrical specimens of 150 × 300 mm and 100 × 200 mm in dimensions are utilized. Training and testing sets of the GEP-based formulations are randomly separated from the complete experimental data. The GEP-based formulations are also validated with additional 173 data of experimental results other than the data used in training and testing sets of the GEP-based formulations. All of the results obtained from the GEP-based formulations are compared with the results obtained from experimental data, the developed regression-based formulation and formulas given by some national building codes. These comparisons showed that the GEP-based formulations appeared to well agree with the experimental data and found to be quite reliable.