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The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

The purpose of this paper is to investigate an optimal control solution with prescribed degree of stability for the position and tracking control problem of the twin rotor multiple input-multiple output (MIMO) system (TRMS). The twin rotor MIMO system is a benchmark aerodynamical laboratory model having strongly non-linear characteristics and unstable coupling dynamics which make the control of such system for either posture stabilization or trajectory tracking a challenging task.

This paper first describes the dynamical model of twin rotor MIMO system (TRMS) and then it adopts linear-quadratic regulator (LQR)-based optimal control technique with prescribed degree of stability to achieve the desired trajectory or posture stabilization of TRMS.

The simulation results show that the investigated controller has both static and dynamic performance; therefore, the stability and the quick control effect can be obtained simultaneously for the twin rotor MIMO system.

The articles on LQR optimal controllers for TRMS can also be found in many literatures, but the prescribed degree of stability concept was not discussed in any of the paper. In this work, new LQR with the prescribed degree of stability concept is applied to provide an optimal control solution for the position and tracking control problem of TRMS.

To develop a non‐linear modelling technique for modern air vehicles with an application to a twin rotor multi‐input‐multi‐output system (TRMS) which resembles the dynamics of a helicopter to a certain extent and presents formidable control challenges.Design/methodology/approach – A Non‐linear AutoRegressive process with eXternal input (NARX) approach with a feedforward neural work and a resilient propagation (RPROP) algorithm is used to model the system. The RPROP algorithm possesses direct weight update capability without considering the size of the partial derivative. The obtained model is shown to be adequate by carrying out convincing tests such as correlations, cross‐validations and prediction based on predicted output and, therefore, is deemed to be reliable.Findings – It is shown that the combination of the feedforward neural networks and RPROP algorithms is very useful and effective in modelling systems with high non‐linearity and other complex characteristics. It is always important to attain a model with minimum number of neurons in different layers of the network by overcoming the possibility of getting stuck in the shallow local minimum of error function by using RPROP algorithm.Research limitations/implications – The system is modelled off‐line. On‐line modelling will be required for real‐time control purpose.Practical implications – The non‐linear modelling approach presented in this study is shown to be appropriately applicable to model new generations' air vehicles and other complex mechatronic systems such as TRMS. So, the approach will be appealing to industrial applications.Originality/value – This paper addresses the problems of modelling modern sophisticated non‐linear systems with complex characteristics and uncertain dynamics.

The 2002 climbing and walking robots (CLAWAR) conference featured 130 papers on topics related to CLAWAR. This article reviews the conference, highlighting papers in the areas of modular design, inspection of tanks, weld inspection, sewer inspection/cleaning, window cleaning, medical robotics, hybrid locomotion and hopping robots.

The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and particle swarm optimization (PSO) as genetically bacterial swarm optimization (GBSO). The implementation of GBSO is illustrated by designing the fuzzy pre‐compensated PD (FPPD) control for two‐link rigid‐flexible manipulator.

The hybridization is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover, and mutation operators. Second, the search direction vector is optimized using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The FPPD controller design objective was to tune the PD controller constants, normalization, and denormalization factors for both the joints so that integral square error, overshoots, and undershoots are minimized.

The proposed algorithm is tested on a set of mathematical functions which are then compared with the basic algorithms. The results showed that the GBSO had a convergence rate better than the other algorithms, reaching to the optimal solution. Also, an approach of using fuzzy pre‐compensator in reducing the overshoots and undershoots for loading‐unloading and circular trajectories had been successfully achieved over simple PD controller. The results presented emphasize that a satisfactory tracking precision could be achieved using hybrid FPPD controller with GBSO.

Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to set of functions considered and it can easily be extended for other global optimization problems. The proposed FPPD controller tuning approach is interesting for the design of controllers for inherently unstable high‐order systems.

The purpose of this paper is to investigate the effect on completion of mobile‐robot tasks depending on how a human tele‐operator interacts with a sensor system and a mobile‐robot.

Interaction is investigated using two mobile‐robot systems, three different ways of interacting with the robots and several different environments of increasing complexity. In each case, the operation is investigated with and without sensor systems to assist an operator to move a robot through narrower and narrower gaps and in completing progressively more complicated driving tasks. Tele‐operators used a joystick and either watched the robot while operating it, or sat at a computer and viewed scenes remotely on a screen. Cameras are either mounted on the robot to view the space ahead of the robot or mounted remotely so that they viewed both the environment and robot. Every test is compared with sensor systems engaged and with them disconnected.

A main conclusion is that human tele‐operators perform better without the assistance of sensor systems in simple environments and in those cases it may be better to switch‐off the sensor systems or reduce their effect. In addition, tele‐operators sometimes performed better with a camera mounted on the robot compared with pre‐mounted cameras observing the environment (but that depended on tasks being performed).

Tele‐operators completed tests both with and without sensors. One robot system used an umbilical cable and one used a radio link.

The paper quantifies the difference between tele‐operation control and sensor‐assisted control when a robot passes through narrow passages. This could be an useful information when system designers decide if a system should be tele‐operated, automatic or sensor‐assisted. The paper suggests that in simple environments then the amount of sensor support should be small but in more complicated environments then more sensor support needs to be provided.

The paper investigates the effect of completing mobile‐robot tasks depending on whether a human tele‐operator uses a sensor system or not and how they interact with the sensor system and the mobile‐robot. The paper presents the results from investigations using two mobile‐robot systems, three different ways of interacting with the robots and several different environments of increasing complexity. The change in the ability of a human operator to complete progressively more complicated driving tasks with and without a sensor system is presented and the human tele‐operators performed better without the assistance of sensor systems in simple environments.

Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues.

PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence.

PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables.

PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

The purpose of this paper is to investigate the effect on time to complete a task depending on how a human operator interacts with a mobile‐robot. Interaction is investigated using two tele‐operated mobile‐robot systems, three different ways of interacting with robots and several different environments. The speed of a tele‐operator in completing progressively more complicated driving tasks is investigated also.

Tele‐operators are timed completing a series of tasks using a joystick to control a mobile‐robot. They either watch the robot while operating it, or sit at a computer and view scenes remotely on a screen. Cameras are either mounted on the robot, or so that they view both the environment and robot. Tele‐operators complete tests both with and without sensors. One robot system uses an umbilical cable and one uses a radio link.

In simple environments, a tele‐operator may perform better without a sensor system to assist them but in more complicated environments then a tele‐operator may perform better with a sensor system to assist. Tele‐operators may also tend to perform better with a radio link than with an umbilical connection. Tele‐operators sometimes perform better with a camera mounted on the robot compared with pre‐mounted cameras observing the environment (but that depends on tasks being performed).

Tele‐operated systems rely heavily on visual feedback and experienced operators. This paper investigates how to make tasks easier.

The paper suggests that the amount of sensor support should be varied depending on circumstances.

Results show that human tele‐operators perform better without the assistance of a sensor systems in simple environments.