Direct Adaptive Control for Underactuated Mechatronic Systems using Fuzzy Systems and Neural Networks: A Pendubot Case

Over the last few decades, fuzzy logic has been shown as a powerful methodology for dealing with imprecision and nonlinearity efficiently. Applications can be found in a wide context ranging from medicine to finance, from human factors to consumer products, from vehicle control to computational linguistics, and so on (Wang 1997; Dubois and Prade 2000; Passino and Yurkovich 1998; Jang et al. 1997; Sugeno 1985; Pedrycz 1993). However, one of the shortcomings of fuzzy logic is the lack of systematic design. To circumvent this problem, fuzzy logic is usually combined with Neural Networks (NNs) by virtue of the learning capability of NNs. NNs are networks of highly interconnected neural computing elements that have the ability of responding to input stimuli and learning to adapt to the environment. Both fuzzy systems and NNs are dynamic and parallel processing systems that estimate input-output functions (Mitra and Hayashi 2000). The merits of both fuzzy and neural systems can be integrated in Fuzzy Neural Networks (FNNs) (Lee and Lee 1974, 1975; Pal and Mitra 1999; Zanchettin and Ludermir 2003). Therefore, the integration of fuzzy and neural systems leads to a symbiotic relationship in which fuzzy systems provide a powerful framework for expert knowledge representation, while NNs provide learning capabilities.

A novel direct adaptive fuzzy neural networks(FNNs)controller for a class of unc ertain nonlinear chaotic system is presented.Takagi-Sugeno fuzzy logic systems a re applied to estimate the unknown function of the chaotic system,then the param eters of adaptive FNNs control law are on-line tuned so that the tracking prob le m is realized,i.e.to force the solutions of the chaotic plant to follow a certai n given trajectory under the condition that all signals involved are uniformly b ounded.Simulation results show the validity of the obtained result.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

Direct adaptive fuzzy position controller for an electropneumatic actuator: Design and experimental evaluation

Cascade fuzzy control of a tubular chemical reactor

Classification of seven kinds of dyeing defects is proposed using image processing and fuzzy neural network approaches. The defects include filling band in shade, dye and carrier spots, mist, oil stain, tailing, listing, and uneven dyeing on selvage. The fuzzy neural classification system is constructed by a fuzzy expert system with the neural network as a fuzzy inference engine. The neural network is trained to become the inference engine using sample data. This fuzzy neural network system possesses merits of both fuzzy logic and neural networks, and thus is more intelligent in handling pattern recognition and classification problems. Region growing is adopted to directly detect different defect regions in an image. In all, seventy samples, ten samples for each defect, are obtained for training and testing. The results demonstrate that the fuzzy neural network approach can precisely classify these samples by the features selected.

This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

On the basis of previous research, This article studies the structure and working principle of recurrent fuzzy neural network. The traditional fuzzy controller is not precise enough, does not have simple algorithm but poor timely traceability. The use of the fuzzy neural network is good for fully integrating the fuzzy control feature and neural network control feature. And we can improve the fuzzy neural network into recurrent fuzzy neural network, and apply this in the power flow control of the power converter. Through simulating and contrasting, the experiments show that it can achieve the UPFC trend of dynamic real-time monitoring, it has better tracking control capability and robustness, and preferable effect of power control.

Typical Takagi–Sugeno PI and PD fuzzy controllers: analytical structures and stability analysis

In this paper mathematical models of Mamdani type fuzzy PI or PD controllers via Center of Gravity (CoG) defuzzification are obtained. These controllers are called the simplest fuzzy PI or PD controllers as minimal number of fuzzy sets (two fuzzy sets on each of the two-input variables and three fuzzy sets on the output variable) are employed. The basic components of these models are L-type and Y-type membership functions on each input variable, trapezoidal or triangular membership functions on output variable, minimum / algebraic product t-norm, maximum OR operator, three linear fuzzy control rules, minimum / algebraic product inference and CoG defuzzification. Mathematical models of fuzzy controller via CoG defuzzification were not reported in the literature and are therefore presented for the first time in this paper.

The AGC of reheat interconnected two area power systems are characterized by non-linearity and uncertainty. A hybrid neural network and fuzzy control is proposed for automatic generation control in power systems. Recurrent neural network is employed to forecast controller and system's future output, based on the current Area Control Error (ACE) and the predicted change-of-ACE. The Control Performance Standard (CPS) criterion is adapted to the fuzzy controller design, thus improves the dynamic quality of system. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% step-load disturbance in area 1 were obtained. The comparison of frequency deviations and tie-line power deviations for the two area interconnected thermal power system integral controller with Redox Flow Batteries (RFB) reveals that the system with hybrid fuzzy neural controller enhances a better stability than that of system without hybrid fuzzy neural controller.

Over the last decade, an extensive research has been carried out in the areas of fuzzy logic and neural networks. Fuzzy logic has emerged as a mathematical tool to deal with the uncertainties in human perception and reasoning. It also provides a framework for an inference mechanism that allows for approximate human reasoning capabilities to be applied to knowledge-based systems. On the other hand, artificial neural networks have emerged as fast computation tools with learning and adaptivity capabilities. Recently, these two fields have been integrated into a new emerging technology called fuzzy neural networks which combines the benefits of each field. The objective of the paper is to establish the similarities and differences between fuzzy systems and neural networks and to discuss possible models for fuzzy neural networks which can be applied to system modeling and control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fuzzy logic and neural networks have gained increased attention as methodologies of control system for managing uncertainty. There is computational equivalence between fuzzy logic and neural network systems. A new model of fuzzy neural network is built. Furthermore, the approach of fuzzy neural network is applied to solving the problem of autonomous control for a teleoperated mobile robot in an unknown environment. The new approach increases the learning speed, fine adjusts the rules, and improves the quality of the system.

In this paper, the direct adaptive fuzzy robust control is applied to the attitude stabilization control of the satellite, and the detailed design procedure of the fuzzy control system is presented. The controller directly acting on the controlled system doesn't require the modeling of the uncertain nonlinear system. The direct adaptive fuzzy controller is designed for the satellite, and the robust control term is added to the system in order to resist the external disturbances. The adaptive law of the rule parameter is derived. Simulation results show that precise attitude control is accomplished in spite of the uncertainty in the system.

In a bioprocess, for example a fermentation process, many biological reactions are always working in intracellular space and the control of such a process is very complicated. Bioprocesses have therefore been controlled by the judgment of the experts who are the skilled operators and have much experience in the control of such processes. Such experience is normally described in terms of linguistic IF-THEN rules. Fuzzy inference is a powerful tool for incorporating linguistic rules into computer control of such processes. Fuzzy control is divided into two types--direct fuzzy control of process variables, for example sugar feed rate and fermentation temperature, and indirect control via phase recognition. In bioprocess control the experts decide the value of controllable process variables such as sugar feed rate or temperature as output data from several state variables as input data. Fuzzy control is regarded as a computational algorithm in which the causal relationship between input and output data are incorporated. In Japan fuzzy control has already been applied to practical industrial processes such as production of pravastatin precursor and vitamin B2 and to the Japanese sake mashing process; these examples are reviewed. In addition, an advanced control tool developed from a study on fuzzy control, fuzzy neural networks (FNN), are introduced. FNN can involve complicated causality between input and output data in a network model. FNN have been proven to be applicable to a research in biomedicine, for example modeling of the complicated causality between electroencephalogram or gene expression profiling data and prognostic prediction. Successful results on this research will be also explained.