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Neural networks and fuzzy logic have the potential to overcome some of the most difficult problems that occur in the design and implementation of modern Flight Control Systems (FCS). Ultimately, this may yield significant gains in performance, robustness, cost survivability and reliability. However, it is still uncertain what neural network and fuzzy logic functions are both technologically feasible and suitable for flight control system implementation. In this report, an ongoing comprehensive program to develop and assess this technology for Naval FCS applications is described. Currently, this program is focused on the development of a neural network FCS design tool, a neural network flight control law emulator, a fuzzy logic automatic carrier landing system and a neural network flight control configuration management system. For each project, some initial results are given. Also, several new and planned projects are discussed. These include learning augmented adaptive control, neural network augmented nonlinear control, optical neurons and neural augmentation of conventional control systems.

A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

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The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate. Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

This paper deals with voltage tracking control of DC- DC boost converter based on Fuzzy neural network. Maintaining the output voltage of the boost converter in some applications are very important, especially for sudden change in the load or disturbance in the input voltage. Traditional control methods usually have some disadvantages in eliminating these disturbances, as the speed of response to these changes is slow and thus affect the regularity of the output voltage of the converter. The strategy is to sense the output voltage across the load and compare it with the reference voltage to ensure that it follows the required reference voltages. In this research, fuzzy neural was introduced to achieve the purpose of voltage tracking by training the parameter of controller based on previous data. These data sets are the sensing input voltage of the converter and the value of the output load changes. To establish the performance of proposed method, MATLAB/SIMULINK environments are presented, simulation results shows that proposed method works more precisely, faster in response and elimination the disturbances.

It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately. 

This paper proposes an improvement of the direct power control (DPC) scheme of a grid connected three phase voltage source inverter based on artificial neural networks (ANN) and fuzzy logic (FL) techniques for the renewable energy applications. This advanced control strategy is based on two intelligent operations, the first one is the replacement of the conventional switching table of a three phase voltage source inverter (VSI) by a selector based on artificial neural networks approach, and the second one is the replacement of the hysteresis comparators by fuzzy logic controllers for the instantaneous active and reactive power errors. These operations enable to reduce the power ripples, the harmonic disturbances and increase the response time period of the system. Finally, the simulation results were obtained by Matlab/Simulink environment, under a unity power factor (UPF). These results verify the transient performances, the validity and the efficiency of the proposed DPC scheme.

This paper presents a new scheme for variable structure (VS) fuzzy PD controller. The rule base of the fuzzy PD controller is tuned online. The purpose of the proposed controller is to track accurately a preselected position command for the servomechanism system. Therefore, this study establishes a model using a black-box modeling approach; simulations were performed based on real-time data collected by LabVIEW and processed using MATLAB. The input signal for the servomechanism driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval. The candidate models were obtained using linear least squares, nonlinear least squares, and deep neural network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors. Then, the DNN identified model was used to design the proposed control techniques. A comparison had been executed between the VS fuzzy PD control, the conventional PD control, and the fixed structure fuzzy PD control. The experimental results confirm the proposed VS fuzzy PD control can absorb the nonlinear behavior of the system. The speed regulation test, it reduces the rise time from 50% to 56%. While continuously changing in speed, it has the smallest tracking error (0.412 inches).

This paper presents a new scheme for variable structure (VS) fuzzy PD controller. The rule base of the fuzzy PD controller is tuned online. The purpose of the proposed controller is to track accurately a preselected position command for the servomechanism system. Therefore, this study establishes a model using a black-box modeling approach; simulations were performed based on real-time data collected by LabVIEW and processed using MATLAB. The input signal for the servomechanism driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval. The candidate models were obtained using linear least squares, nonlinear least squares, and deep neural network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors. Then, the DNN identified model was used to design the proposed control techniques. A comparison had been executed between the VS fuzzy PD control, the conventional PD control, and the fixed structure fuzzy PD control. The experimental results confirm the proposed VS fuzzy PD control can absorb the nonlinear behavior of the system. The speed regulation test, it reduces the rise time from 50% to 56%. While continuously changing in speed, it has the smallest tracking error (0.412 inches).

The aim is to create an intelligent control system based on soft computing for controlling dynamic objects moving along one of the defined routes in real-time systems. The parameters of objects in the real environment are characterized by high nonlinearity, dependence on the state of the environment, and time-varying dynamics when some parameters and states of objects are not available for measurement. Taking this into account, the hierarchical structure of the system is developed based on the classical fuzzy algorithms of Mamdani and Takagi-Sugeno-Kang, and an adaptive fuzzy neural network that implements the model. The application of a neuro-fuzzy model to controlling the movement of dynamic objects with many parameters and incomplete certainty through the use of expert knowledge is considered. A mathematical description of the fuzzy hierarchical model, a learning algorithm, and computer modeling are presented on the example of controlling the speed of rolling cars from a sorting hill. An example of the application of fuzzy rules built on numerical data is considered. The results of modeling with visualization of the results for the synthesized data are presented. The scientific innovation of the obtained results lies in the development of a hierarchical neuro-fuzzy model designed for forecasting and controlling dynamic objects. The modeling results confirm the ability of the proposed model to predict the unknown mapping of the input data vector, which consists of measured and unmeasured parameters, into the desired numerical value at certain points of the path at the model output. The obtained results demonstrate effective prediction of motion dynamics, the ability to achieve high forecasting accuracy and the possibility of intellectualizing the control of the technological process. When approximating the nonlinear dependence, the use of a multilayer neural network ensures the adaptability of the model to a specific area of application, and synergy with a fuzzy algorithm allows automating the process of controlling the technological process at the level of a human operator.