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This paper describes a software architecture which supports the design of hierarchical controllers that provide facilities for adaptation, supervision and task planning. It details how this form of functional hierarchy differs from the structural hierarchy also inherent within a complex control system. Then, both forms of hierarchy are combined in a single design notation and development methodology. The system utilises intelligent control techniques (neuro‐fuzzy and genetic optimisation) for controlling a cryogenic plant used for superconductor testing by cooling the test samples to temperatures below 1008K. The system supports the design of a hierarchical controller that provides facilities for adaptation, supervision and task planning. Simulation results are presented.

The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties.

The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model.

According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties.

This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

The aim of the paper is to develop a novel genetic algorithm (GA)‐based supplementary NeuroFuzzy damping control system for the unified power flow controller (UPFC).

The designed scheme employs a micro‐GA (μ‐GA) to avoid being trapped in a local minimum as opposed to the use of the classical back‐propagation technique. The scheme also uses the “Grand‐Parenting” technique for seeding the initial population to hasten the GA convergence speed. To further speed up the GA for solving the optimization problem, a parallel μ‐GA scheme is also used.

It has been discovered that a parallel μ‐GA scheme with three computers setup is approximately three times faster than the μ‐GA with a single computer node. Also when μ‐GA is integrated with the “Grand‐Parenting” technique for seeding the initial population, it would hasten the convergence speed. The control scheme exhibits strong robustness and excellent damping performance when tested on a multi‐machine power system.

Presentation of a novel NeuroFuzzy‐based UPFC that exhibits strong robustness and excellent damping performance.

Expert systems technology as an area of artificial intelligence is coming to the field of structural mechanics. A number of expert systems have been developed or are under development. This paper consists of two parts. A brief discussion of the basics of expert systems and their concepts is given in the first part. The second part reviews the prototype of expert systems developed as an aid for finite element analysis and design optimization. Twelve different expert systems are described. A partial list of books on expert systems in general is given in the Appendix.

This paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls.

The one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles.

Simulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions.

By comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.

Almost all industrial systems are distributed with multiple control points which interact to a limited extent, for which the idea of distribution of task at local (field) level is emerging. As locally‐based application tasks can reduce control delays, a fieldbus‐based smart and reliable DCS solution is recognised as a leader for real‐time industrial automation. Advanced control system has turned itself towards the implementation of digital distributed control systems (DCS) from centralised control systems. The phenomenon is becoming very popular because of its advantages over the whole operating system. Presents a case study for realising manufacturing systems (production lines) with fieldbus technology. The local operating network (LON) fieldbus system was chosen for this purpose because of availability of a wide range of products. Emphasises the reliability aspects of the control systems. A representative of a conveyor system, integrated with field devices, was conceived as the target platform.

This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.

Conventional FACTS based stabilizers are decentralized controllers that adopt local measurements and operate in closed loop. To improve overall system dynamic performance, a coordinating application of FACTS based stabilizer is essential. Although, numerous researches have indicated the effectiveness and superiority of fuzzy logic controllers in comparison with the conventional linear controllers in power system application but researchers have not adequately investigated coordination of multi fuzzy controllers in multi‐machine power systems to provide optimal performance. Genetic algorithm is used to determine optimum values of controllers' parameters.

The search space of the optimisation procedure is decreased to a smaller one, design and computation time can be reduced significantly and the design process becomes more systematic.

A systematic method is introduced to coordinate multi fuzzy FACTS based stabilizers in multi‐machine power systems.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

This paper presents an investigation into the classical problem using an experimental apparatus and applying an artificial neural network (NN) optimisation approach for determining the pressure distribution in a journal bearing at elasto‐hydrodynamic state of lubrication. The experimental system is employed at different working speeds with different surface roughness of shafts for getting pressure distribution. A NN is employed to predict the real data of the system as an optimisation technique. The NN is a radial basis function back propagation network. The NN has a superior performance to follow the desired results of the system and is employed to analyse such systems parameters in practical applications.