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The purpose of this paper is to discuss the application of computational intelligence techniques to the field of industrial robot control.

The core ideas behind using neural computation, evolutionary computation, and fuzzy logic techniques are presented, along with a selection of specific real‐world applications.

Their practical advantages and disadvantages relative to more traditional approaches are made clear.

The reader will appreciate the power of computational intelligence techniques for industrial robot control, and hopefully be encouraged to explore further the possibility of using them to achieve improved performance in their own applications.

Micro aerial vehicle is nonlinear plant; it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances.

First, by employing interval type-II fuzzy neural network (ITIIFNN) to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of micro aircraft vehicle (MAV). Then, the Lyapunov stability theorem is used to testify the asymptotic stability of the closed-loop system, the parameters of the ITIIFNN and gain of sliding mode control can be tuned on-line by adaptive laws based on Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system.

The validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of interval type-II fuzzy neural network based gain adaptive sliding mode controller (GASMC-ITIIFNN) is significantly improved compared with conventional adaptive sliding mode controller (CASMC), type-I fuzzy neural network based sliding mode controller (GASMC-TIFNN).

This approach has been used in one MAV, the controller works well, and which could guarantee the MAV control system with good performances under uncertainties, variations, and external disturbances.

The main original contributions of this paper are: the proposed control scheme makes full use of the nominal model of the MAV attitude control model; the overall closed-loop control system is globally stable demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the MAV attitude control system based on GASMC-ITIIFNN controller can achieve favourable tracking performance than GASMC-TIFNN and CASMC.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot.

The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration.

This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online.

Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

Presents a switching type neuro‐fuzzy control synthesis. The control algorithm supposes as a component part a neurocontrol designed to optimize a performance index. Whenever the neurocontrol saturates or a certain performance parameter of the system decreases, the scheme of control switches to a feasible and reliable fuzzy logic control. Describes the procedure of return to the optimizing neurocontrol which is essential. This methodology of control synthesis ensures antisaturating, antichattering and robustness properties of the controlling system, as illustrated by numerical simulation in the case of a primary flight controls electrohydraulic servo actuator

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller.

The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure.

First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem.

The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

The proportional‐integral‐derivative (PID) controller has been a practical application in industry due to its simple architecture, being easily designed and its parameter tuning without complicated computation. However, the traditional PID controller usually needs some manual retuning before being used for practical application in industry. The purpose of this paper is to propose an auto‐tuning PID controller (ATPIDC) which can automatically tune the controller parameters based on the gradient descent method and the Lyapunov stability theorem. Finally, a field‐programmable gate array (FPGA) chip is adopted to implement the proposed ATPIDC scheme for possible low‐cost and high‐performance industrial applications, and it is applied to a DC servomotor to show its effectiveness.

To ensure the stability of the intelligent control system, a compensator usually should be designed. The most frequently used compensator is designed as a sliding‐mode control, which results in substantial chattering in the control effort. To tackle this problem, the proposed ATPIDC system is composed of a PID controller and a fuzzy compensator. The PID controller can automatically tune the gain factors of the controller gains based on the gradient descent method, and the fuzzy compensator is utilized to eliminate approximation error based on the Lyapunov stability theorem. The proposed fuzzy compensator not only can remove the chattering phenomena of conventional sliding‐mode control completely, but also can guarantee the stability of the closed‐loop system.

The proposed ATPIDC system is applied to a DC servomotor on a FPGA chip. The hardware implementation of the ATPIDC scheme is developed in a real‐time mode. Using the FPGA to implement, the ATPIDC system can achieve the characteristics of small size, fast execution speed and less memory. A comparison among the fuzzy sliding‐mode control, adaptive robust PID control and the proposed ATPIDC is made. Experimental results verify a better position tracking response can be achieved by the proposed ATPIDC method after control parameters training.

The proposed ATPIDC approach is interesting for the design of an intelligent control scheme. An on‐line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable tracking performance and no chattering phenomena can be achieved by using the proposed ATPIDC system. Also, the proposed ATPIDC methodology can be easily extended to other motors.

The purpose of this paper is to design an online-data driven adaptive control scheme based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems.

By using the input-output characteristic curve of controlled plant and the set of IF-THEN rules based on human knowledge inspiration, the adaptive controller is established by an adaptive FREN. The learning algorithm is established with convergence proof of the closed-loop system and controller’s parameters are directly designed by experimental data.

The convergence of tracking error is verified by the theoretical results and the experimental systems. The experimental systems and comparison results show that the proposed controller and its design procedure based on input-output data can achieve superior performance.

The theoretical aspect and experimental systems with the light-emitting diode (LED) current control and the robotic system prove that the proposed controller can be designed by using only input-output data of the controlled plants when the tracking error can be affirmed the convergence.

The proposed controller has been theoretically developed and used through experimental systems by using only input-output data of the controlled plant. The novel design procedure has been proposed by using the input-output characteristic curve for both positive and negative control directions.

The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization realized by means of the Multiobjective Particle Swarm Optimization (MOPSO).

In fuzzy modeling, complexity, interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. Since the performance of the IG‐RBFNN model is directly affected by some parameters, such as the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials in the consequent parts of the rules, the authors carry out both structural as well as parametric optimization of the network. A multi‐objective Particle Swarm Optimization using Crowding Distance (MOPSO‐CD) as well as O/WLS learning‐based optimization are exploited to carry out the structural and parametric optimization of the model, respectively, while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.

The performance of the proposed model is illustrated with the aid of three examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

A MOPSO‐CD as well as O/WLS learning‐based optimization are exploited, respectively, to carry out the structural and parametric optimization of the model. As a result, the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.