Intelligent Adaptive Control Research Articles

A Simulation Tool for the Optimization of Lumber Drying Schedules

A two-dimensional wood drying model based on the water potential concept is used to simulate the convection batch drying of lumber at conventional temperature. The model computes the average drying curve, the internal temperature and moisture content profiles, and the maximum effective moisture content gradient through board thickness. Various scenarios of conventional kiln-drying schedules are tested and their effects on drying time, maximum effective moisture content gradient, final moisture content distribution within and between boards, and energy consumption are analyzed. Simulations are performed for two softwood species, black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). The simulation results indicate that the predictive model can be a very useful tool to optimize kiln schedules in terms of drying time, energy consumption, and wood quality. Such a model could be readily combined with intelligent adaptive kiln controllers for on-line optimization of the drying schedules.

A hybrid control scheme of adaptive and variable structure for flexible spacecraft

As for the control of flexible space structure, in order to get both good dynamic characteristics and good static characteristics, the hybrid control scheme that this paper is put forward with variable structure control method and intelligent adaptive control method. The planar motion of a flexible structure is concerned with, which consists of a rigid central body and a flexible beam attached to it. In view of typical control requirements, corresponding hybrid control scheme is developed. Finally, by comparing the simulation results of hybrid control scheme and variable structure control scheme, it is proved that both the dynamic characteristics and static characteristics of hybrid control scheme presented in this paper are excellent.

Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.

Adaptive Intelligent Control of Economical Flows by Stochastic Dynamics

Abstract Economical flows are studied by means of a particle model. Simulation methods are applied to the problem and a theoretical framework is constructed. Adaptive methods for the analysis, the evaluation and the control of economical flows are deduced.

Genetically optimized neuro-fuzzy IPFC for damping modal oscillations of power system

An integrated approach of radial basis function neural network (RBFNN) and Takagi-Sugeno (TS) fuzzy scheme with a genetic optimization of their parameters has been developed in this paper to design intelligent adaptive controllers for improving the transient stability performance of power systems. At the outset, this concept is applied to a simple device such as thyristor-controlled series capacitor (TCSC) connected in a single-machine infinite bus power system and is then extended to interline power-flow controller (IPFC) connected in a multimachine power system. The RBFNN uses single neuron architecture and its parameters are dynamically updated in an online fashion with TS-fuzzy scheme designed with only four rules and triangular membership function. The rules of the TS-fuzzy scheme are derived from the real- or reactive-power error and their derivatives either at the TCSC or IPFC buses depending on the device. Further, to implement this combined scheme only one coefficient in the TS-fuzzy rules needs to be optimized. The optimization of this coefficient as well as the coefficient for auxiliary signal generation is performed through genetic algorithm. The performance of the new controller is evaluated in single-machine and multimachine power systems subjected to various transient disturbances. The new genetic-neuro-fuzzy control scheme exhibits a superior damping performance as well as a greater critical clearing time in comparison to the existing PI and RBFNN controller with updating of its parameters through the extended Kalman filter (EKF). Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Index Terms-Damping modal oscillations, FACTS, fuzzy, genetic, intelligent controller, neural, power system, stability.

Genetically Optimized Neuro-Fuzzy IPFC for Damping Modal Oscillations of Power Systems

An integrated approach of the radial basis function neural network (RBFNN) and Takagi-Sugeno (TS) fuzzy scheme with genetic optimization of their parameters has been developed in this paper to design intelligent adaptive controllers for improving the transient stability performance of power system. At the outset this concept is applied to a simple device such as the thyristor controlled series capacitor (TCSC) connected in a single-machine infinite bus power system and is then extended to the interline power flow controller (IPFC) connected in a multimachine power system. The RBFNN uses single neuron architecture and its parameters are dynamically updated in an online fashion with the TS-fuzzy scheme designed with only four rules and triangular membership function. The rules of the TS-fuzzy scheme are derived from the real or reactive power error and their derivatives either at the TCSC or IPFC buses depending on the device. Further, to implement this combined scheme only one coefficient in the TS-fuzzy rules needs to be optimized. The optimization of this coefficient, as well as the coefficient for auxiliary signal generation, is performed through genetic algorithm. The performance of the new controller is evaluated both in single-machine and multimachine power systems subjected to various transient disturbances. The new genetic-neuro-fuzzy control scheme exhibits a superior damping performance as well as a greater critical clearing time in comparison to the existing PI as well as RBFNN controller with updating of its parameters through the extended Kalman filter (EKF).

Adaptive-intelligent control of intelligence flows as microheat transfer

From the point of view of microheat transfer, adaptive-intelligent controls (AIC) of intelligence flows by neural-network (NN)- and genetic-algorithm (GA)-systems are studied to breakthrough towards brain computing. Characteristics of information flows in NN- and GA-systems are derived. As an application computer-simulation system for nanostructured materials is constructed. AIC of intelligence flows are discussed.

A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes

Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamically-constructed, feedback fuzzy neural controller (DCF-FNC) from the input–output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC significantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally efficient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.

Ergonomic systems and methods of providing intelligent adaptive surfaces and temperature control

Ergonomic systems and methods of providing intelligent adaptive surfaces and temperature control

A Method of Designing Intelligent Adaptive Control Systems on the Basis of Transition Relation between Models

An approach to designing intelligent adaptive control systems was proposed, and the logic-semiotic apparatus underlying them was described. An example of constructing the control model of a robot was presented. The development software for creating intelligent adaptive control systems was described.

Intelligent Adaptive Control of a Tailless Advanced Fighter Aircraft Under Wing Damage

In this paper we propose an intelligent adaptive recone gurable control scheme for a Tailless Advanced Fighter Aircraft (TAFA) in thepresence of wing damage. The schemeconsists offailuredetection using multipleobservers, multiple reference models for changing the desired performance on-line in the case of damage, on-line estimation ofthepercentage-of-damage parameter, and the corresponding adaptiverecone gurablecontroller augmented with an output error feedback term. The overall scheme results in fast and accurate failure detection and identie cation and control recone guration and is demonstrated analytically to be stable in the sense that all signals are bounded and the output error converges to zero asymptotically even in the case of 100% wing damage. The properties of the proposed intelligent adaptive controller are evaluated through numerical simulations on a TAFA model in thepresence of largenoise, actuatordynamics, position and ratelimitson the control effectors, and 100% wing damage, and the proposed approach results in excellent overall performance.

Intelligent adaptive active control of noise and vibration

This paper reports on recent advances in the development of intelligent techniques for active control of noise and vibration. A self-tuning control mechanism is developed within a feedforward control structure. The scheme is realised with conventional recursive least squares methods, genetic algorithms and neural networks and its performance verified in the cancellation of noise in free-field and vibration suppression in a flexible beam. An iterative adaptive control scheme is then developed and realised within a feedback/feedforward control structure for vibration suppression in a flexible plate. It is demonstrated that significant reduction in the level of noise vibration is achieved with these schemes over a broad range of frequencies.

Intelligent adaptive control in milling processes

In this paper, two intelligent adaptive controllers for milling processes are proposed. One is an intelligent adaptive controller with optimization (IACO) developed based on a neural network and genetic algorithm. The other is an intelligent adaptive controller with constraints (IACC) developed based on a neural network and expert rules. In the IACO, a modified back-propagation neural network (MBPNN), in which a dynamic factor is attached and the learning rate can be adjusted in the learning process is used for the online modelling of the milling system. In addition, a modified genetic algorithm (MAG), in which the search domain can be adjusted in every generation is used for the real-time optimal control of the milling process. In IACC, a simplified BP algorithm is used to learn online, the reverse function of the milling system and realize the real-time adaptive control in the milling process; some expert rules are combined in the BP neural network controller so as to ensure the reliability and stability of the adaptive milling system. The experimental results show that not only does the milling system with the intelligent adaptive controllers have high robustness and global stability, but also the machining efficiency of the milling system with the intelligent adaptive controllers is much higher than the traditional CNC milling system.

Heterogeneous intelligent control systems

Intelligent control of complex industrial processes requires that knowledge from a variety of sources be used to maintain control over an extended set of operating conditions. It is asserted that to maintain the integrity of such heterogeneous knowledge, it should be encoded in distinct models. Control then consists of selecting and executing the most appropriate model for a given situation. Adaptive intelligent control can then be implemented by developing switching strategies that allow a trade-off between the various model properties. A prototype system (MuRaLi) is presented that has multiple models based on three primitive dimensions: precision, scope and generality. Generality is realised through three different knowledge representation mechanisms: procedures, rules and equations. Homogeneous control consists of constant generality and variable precision and scope to generate the most appropriate control action. Heterogeneous control consists of potential variations in all three dimensions. Simple switching strategies are investigated for both forms of control. The system has been applied to the control of a simulated 800 MW thermal power plant. Examples of homogeneous and heterogeneous control are given, with experimental results of adaptation based on the proposed switching strategies.

Adaptive-intelligent control by neural-net systems

Adaptive-intelligent control by neural-net systems is discussed. Actual adaptive-intelligent control is realized in a general system through the following two hierarchical steps: (1) choosing a hierarchical coordinate system (associated with the environment of the system) and constructing the hierarchical evaluation functions (specifying its control states) and (2) finding a set of the most appropriate hierarchical values for the control parameters (giving the minimum value to the evaluation function). Step 1 establishes intelligently self-controllable (thinking) algorithms with human-like intelligence for various events (concepts). Step 2 studies the intelligently self-controllable (thinking) algorithms for finding the most appropriate state. Adaptive-intelligent control by neural-net systems is realized by integrating both intelligently self-controllable (thinking) algorithms on the neural-net systems. Here step 2 is mainly discussed in the neural-net systems of Boltzmann type machines using the method of stochastic dynamics.

161 Intelligent adaptive control of a multi-machine power system: B.W. Hogg, S. Jovanovic, B. Fox, E. Swidenbank, pp 779–782

161 Intelligent adaptive control of a multi-machine power system: B.W. Hogg, S. Jovanovic, B. Fox, E. Swidenbank, pp 779–782

147 Adaptive and supplementary intelligent control of power system stabilizers

147 Adaptive and supplementary intelligent control of power system stabilizers

Intelligent Adaptive Control of a Multi-Machine Power System

The paper presents an intelligent adaptive turbine control scheme intended to improve the overall control performance of a multi-machine power system. Supplementary rule-based control is applied to the turbine governor, taking account of generator acceleration/deceleration as a measure of the imbalance between mechanical and electrical power. It is shown that this approach is capable of achieving more effective control of frequency transients on multi-machine power systems.

Artificial neural network approach in printed circuit board assembly

In this study, automation of the circuit board assembly process is considered using artificial neural networks with knowledge-based systems. Basic issues in achieving intelligent control that can adapt to changing conditions in the assembly process are discussed. The feasibility of using neural networks for pattern recognition and optimum component insertion sequence generation is examined. The study provides a basic foundation for designing a conceptual architecture for adaptive intelligent control of circuit board assembly. Real-time testing of component recognition is conducted using adaptive resonance theory (ART 1) as a neural network paradigm.