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This paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.

As inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.

As a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.

In this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.

This paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed communication topology.

The dynamic leader case is considered while only a subset of the follower mechanical systems can obtain the leader information. First, this paper approximates the system uncertainties with finite fuzzy rules and proposes a distributed adaptive tracking control scheme. Then, this paper makes a detailed classification of the system uncertainties and uses different fuzzy systems to approximate different kinds of uncertainties. Further, an improved distributed tracking strategy is proposed. Closed-loop systems are investigated using graph theory and Lyapunov theory. Numerical simulations are performed to verify the effectiveness of the proposed methods.

Based on fuzzy control and adaptive control theories, the desired distributed coordinated tracking control strategies for multiple uncertain mechanical systems are developed.

Compared with most existing literature, the proposed distributed tracking algorithms use fuzzy control and adaptive control techniques to cope with system nonlinear uncertainties of multiple mechanical systems. Moreover, the improved control strategy not only reduces fuzzy rules but also has higher control accuracy.

The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator. This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators. The uncertainties include parametric uncertainty, un-modeled dynamics, discretization error and external disturbances.

The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory. A robust control term is used to compensate the approximation error of the fuzzy system. An adaptive mechanism is derived based on the stability analysis.

The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators. The approximation error of the fuzzy system is well compensated to achieve asymptotic tracking of the desired trajectories. Stability analysis and simulation results show its efficiency in the tracking control.

A novel discrete indirect adaptive fuzzy controller is designed for electrically driven robot manipulators using the voltage control strategy. The novelty of this paper is compensating the approximation error of the fuzzy system and discretizing error for asymptotic tracking of the desired trajectory.

To design effective and practical controllers that use the adaptive fuzzy approaches and are applicable to helicopters.

Based on Takagi‐Sugeno fuzzy systems, a new direct adaptive fuzzy control scheme is developed for a class of nonlinear multiple‐input‐multiple‐output systems. A simple observer is designed to generate an error signal for the adaptive law. The system states of the system are not required to be available for measurement.

The overall adaptive scheme guarantees all the signals involved being uniformly bounded in the Lyapunov sense.

The implementation of this research work needs further investigation.

The simplicity of the design algorithm facilitates the application of the design to helicopters by the use of Matlab.

Experimental results of a two degree of freedom helicopter are presented to confirm the usefulness of the proposed new control scheme.

The purpose of this paper is to propose a kind of fuzzy adaptive control method to control non‐linear system that has the characteristic of small time delay and fast respond speed.

The paper analyzes the production process and the actual condition of the preheat process of the plating zinc and painting plastic scribbled of double layer welded pipe that has the small time delay and fast respond speed, and also gives the preheat process mathematical model. Fuzzy adaptive control method with hierarchical structure is used which aims at one non‐linear system that has the characteristic of small time delay and fast responds speed. Through the simulation, it proves the mentioned method is effective to control the temperature system for double layers welded pipe in welding process.

Based on the mathematical model proposed about the production process and the actual condition of the preheat process, the fuzzy adaptive control method is effective to control the temperature system for double layers welded pipe in welding process.

The paper proposes fuzzy adaptive control method with hierarchical structure which has the basic fuzzy control grade, adaptive adjust grade, and process state judgment grade.

A very useful method in welding process for double layers welded pipe.

The new mathematical model is proposed about the production process, and the new control method is used in the temperature system for double layers welded pipe in welding process.

The purpose of this paper is to investigate the stabilization of unstable periodic orbits of Chua’s system using adaptive fuzzy sliding mode controllers with moving surface.

For this aim, the sliding mode controller and fuzzy systems are combined to achieve the stabilization. Then, the authors propose a moving sliding surface to improve robustness against uncertainties during the reaching phase, parameter variations and extraneous disturbances.

Afterward, the authors design a sliding observer to estimate the unmeasurable states which are used in the previously designed controller.

Numerical results are provided to show the effectiveness and robustness of the proposed method.

The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies.

The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy.

The results of the experiment showed that FSC produced the best F₁-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system.

Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness.

This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

The purpose of this paper is to focus on the evaluation model of the enterprises' technological innovation system, based on the theory of complex adaptive system.

Combined with the status quo and recent studies of Chinese enterprises' technological innovation, the paper discusses the complex‐system features of the technological innovation. The stimulus‐response model is used to establish the two‐level framework for enterprises' technological innovation system. By means of the adaptive fitness function, the economic and social utility of enterprises' technological innovation is measured from two dimensions. Finally, the fuzzy catastrophe model is introduced to evaluate the enterprises' technological innovation.

The enterprises' technological innovation system has attributions of the subject aggregation, the systematic openness, nonlinearity and diversity. Thus, the macro‐micro based technological innovation system from the perspective of complex adaptive system is proposed. The system utility is considered based on the system subjects and system structure, and the calculation framework of the adaptive fitness for the whole system is obtained by considering the emergent property describing the system scale effect and structure effect. In fact, the fuzzy theory can well reflect the influential situation that the interactions between different factors may cause the mutation of the higher level and the interactions between enterprises can lead to the shifts of the system.

The paper proposes the complex adaptive system for the enterprises' technological innovation based on the special macro environment in China. A new framework for the research of technological innovation is provided by analyzing the system inner model. Fuzzy catastrophe model can reduce the evaluation irrationality due to the subjective index weights.

This paper aims to present an adaptive fuzzy sliding mode controller with nonlinear observer (AFSMCO) for the redundant robotic manipulator handling a varying payload to achieve a precise trajectory tracking in the task space. This approach could be applied to solve the problems caused by the dynamic effect of the varying payload to robotic system caused by model uncertainties.

First, a suitable observer using the recursive algorithm is presented for an accurate estimation of external disturbances caused by a variable payload. Second, the adaptive fuzzy logic is designed to approximate the parameters of the sliding mode controller combined with nonlinear observer (SMCO) to avoid chattering in real time. Moreover, Lyapunov theory is applied to guarantee the stability of the proposed closed-loop robotic system. Finally, the effectiveness of the proposed control approach and theoretical discussion are proved by simulation results on a seven-link robot and demonstrated by a humanoid robot platform.

The varying payload leads to large variations in the dynamics of the manipulator and the tracking error. To achieve high-precision position tracking, nonlinear observer was introduced to feed into the sliding mode control (SMC) which had improved the ability to resist the external disturbance. In addition, the chattering caused by the SMC was eliminated by recursively approximating the switching gain with the usage of adaptive fuzzy logic. Therefore, a distributed control strategy solves the problems of an SMC implementation in improving its tracking performance and eliminating the chattering of the system control.

The AFSMCO is proposed for the first time and used to control the redundant robotic manipulator that handles the varying payload. The proposed control algorithm possesses better robustness and higher precision for the trajectory tracking than classical SMC.

Discrete control of robot manipulators with uncertain model is the purpose of this paper.

The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm.

The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control.

A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.