This paper proposes an event-triggered, observer-based adaptive output-feedback control scheme for a class of nonlinear systems with partially unavailable states, formulated using a General Type-2 Takagi–Sugeno Fuzzy Neural Network (GT2-TS-FNN). The proposed framework comprises two components: an intelligent event-triggered full-state observer that estimates the unmeasurable states and a fuzzy output-feedback controller, both constructed based on the GT2-TS-FNN. To decompose a General Type-2 Fuzzy Set into several Interval Type-2 Fuzzy Sets, an efficient α-cut strategy is employed. In addition, a direct defuzzification method is adopted to reduce the computational burden associated with type reduction, thereby avoiding the iterative Karnik–Mendel algorithm. Based on Lyapunov stability theory, an adaptive updating law is derived to ensure fast convergence and guarantees the stability of the overall closed-loop system. The proposed scheme is further implemented on a microcontroller for the real-time speed control of an induction-motor drive operating over a networked communication protocol, where the event-triggered mechanism significantly reduces both the communication frequency and the computational load. Both simulation and experimental results confirm improved tracking performance under random disturbances, external load variations, and sensor-dropout conditions.

This paper introduces a probabilistic Takagi-Sugeno-Kang fuzzy neural network (PTSK-FNN) within a reliable indirect adaptive control framework that updates the gains of proportional - integral - derivative (PID) controller. The reasons for introducing this study include effective management of chaotic uncertainties by integrating the probabilistic processing with TSK fuzzy neural system, improved system identification needed for calculating control signals, and a novel law for an online learning algorithm based on the Lyapunov theorem to ensure system stability. The proposed controller requires a sensitivity function derived from the system model, which can be obtained through identification techniques utilizing Wiener model based on PTSK-FNN for modeling both linear and nonlinear dynamics of the system. By dynamically modifying both the structure and parameters of the PTSK-FNNs, the PID controller gains are updated, leading to enhance control performance. This control strategy is implemented for nonlinear dynamic systems and compared with other existing controllers, demonstrating its effectiveness in engineering applications. Simulation and experimental results indicate that the proposed controller significantly outperforms its alternatives in mitigating random noise, external disturbances, and system uncertainties. The proposed controller shows minimum performance indices compared to other published controllers, achieving improved performance by reducing the mean absolute error by 34.2 % in simulations and 38.6 % in experimental results, compared to higher-performing published controllers.

This study introduces a two-degree-of-freedom (2-DOF) fuzzy proportional-derivative (FPD) controller for controlling a robot manipulator practically. The suggested controller combines the advantages of a 2-DOF structure and the FPD structure to obtain an intelligent controller, which can reduce the system’s uncertainties. These uncertainties are unknown disturbances, variation, payload, and cross-coupling effects between joints. The analytical structure for the developed scheme is derived to show the relationship between the inputs and output of the controller. Moreover, the sufficient conditions for the bounded-input bounded-output (BIBO) stability of the proposed control system have been performed based on the well-known small gain theorem. The proposed 2-DOF FPD is designed experimentally using a microcontroller. The proposed controller's performance is compared with conventional PID, 2-DOF PID, and fuzzy PD controllers through simulations and practical experiments. The 2-DOF FPD controller achieved a 20–30

This paper proposes an adaptive tracking scheme for a 4-wheeled skid steering mobile robot (4-WSSMR) using diagonal recurrent neural network controller based on the hybrid deep learning algorithm (DRNNC-HDLA). For the developed DRNNC-HDLA structure, the diagonal recurrent neural network is constructed, whose initial weights values are obtained through the hybrid deep learning algorithm. It is a combination of the restricted Boltzmann machine and the self-organized map of Kohonen. The network weights and learning rate for the proposed scheme are updated based on the Lyapunov stability criteria to achieve the controlled system stability. To show the robustness of the proposed algorithm, the results are compared to other existing algorithms. The proposed algorithm is practically implemented for controlling a 4-WSSMR to show the ability of the proposed algorithm to deal with real applications. The effectiveness of the proposed approach is validated through extensive real-world experiments involving uncertainties and disturbances, demonstrating its capability to achieve accurate and reliable trajectory tracking. This work advances the field by offering a reliable control solution for mobile robots operating under challenging conditions.

In this paper, a novel adaptive fuzzy controller based on deep reinforcement learning (DRL) is introduced for electro-hydraulic servo systems. The controller combines the strengths of fuzzy proportional–integral (PI) control and deep Q-learning network (DQLN) to achieve real-time adaptation and improve the control performance. The purpose of this paper is to address the challenges of controlling electro-hydraulic servo systems by developing an adaptive controller that can dynamically adjust its control parameters based on the system’s state. The traditional fuzzy PI controller is enhanced with DRL techniques to enable automatic adaptation and compensation for changing online conditions. The proposed adaptive controller utilizes a DQLN to dynamically adjust the scaling factors of the input/output membership functions. By using the DQLN algorithm, the controller learns from a variety of system data to determine the optimal control parameters. The update equation of the weights for the Q-network is derived using the Lyapunov stability (LS) theorem, which overcomes the limitations of gradient descent (GD) methods such as instability and local minima trapping. To evaluate the effectiveness of the proposed controller, it is practically implemented to regulate an electro-hydraulic servo system. The controller’s performance is compared against other existing controllers, and its enhancements are demonstrated through experimental evaluation.

This paper introduces an adaptive controller based on a bilinear quantum recurrent neural network (AC-BLQRNN). The proposed neural structure uses five bilinear quantum neurons in the input layer, one quantum neuron in the hidden layer, and one linear neuron is used for the output layer. Thus, the proposed algorithm consists of a (5–1–1) neural structure with only nine tunable parameters that aims to reduce the computation time. Moreover, it merges the merits of the bilinear neural network that requires a little number of hidden neurons and the powerful processing of the quantum neurons. In addition, a BLQRNN identifier is designed with the same proposed neural network structure, i.e., (5–1–1) to find the sensitivity function. All adjustable parameters in the proposed structure are learned using an updating rule derived using the Lyapunov stability criterion to stabilize the proposed learning algorithm. The proposed AC-BLQRNN is implemented practically and applied to a nonholonomic two-wheel mobile robot (NHWMR) to control the wheels' speeds for tracking a specific trajectory. Moreover, several tasks are performed on NHWMR to investigate the robustness of the proposed AC-BLQRNN controller. The experimental results indicate the powerful computing, fast convergence, and robust performance of the proposed AC-BLQRNN over other existing controllers.

An adaptive fractional-order sliding mode control (AFOSMC) is proposed to control a nonlinear fractional-order system. This scheme combines the features of sliding mode control and fractional control for improving the response of nonlinear systems. The structure of AFOSMC includes two units: fractional-order sliding mode control (FOSMC) and the tuning unit that employs a certain Takagi–Sugeno–Kang fuzzy logic system for online adjusting the parameters of FOSMC. Tuning the parameters of the FOSMC improves its performance with various control problems. Moreover, stability analysis of the proposed controller is studied using Lyapunov theorem. Finally, the developed control scheme is introduced for controlling a fractional-order gyroscope system. The proposed AFOSMC is implemented practically using a microcontroller where the test is carried out using the hardware-in-the-loop simulation. The practical results indicate the improvements and enhancements introduced by the developed controller under external disturbance, uncertainties and random noise effects.

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs.

In this paper, a hybrid deep learning neural network controller (HDLNNC) for nonlinear systems is proposed. The proposed controller structure consists of a multi-layer feed-forward neural network, which can be trained based on the hybrid deep learning. The Lyapunov stability criterion is used to develop an adaptive learning rate due to the learning rate of the updating parameters plays a worthy role in achieving the stability of a system. To show the robustness of the proposed controller and its performance, several tests such as disturbance signals and parameter variations are carried on a numerical example. In this concern, the practical implementation of the proposed HDLNNC is executed on a real system. The results indicate that the proposed controller is able to improve the system performance compared with other existing controllers.

In this paper, an adaptive proportional-integral-derivative (PID) controller based on a quantum neural network (APIDC-QNN) is introduced. In the proposed neural network structure, three neural layers are used namely: input layer, hidden layer, and output layer. The input and hidden layers use three and six quantum-processing neurons respectively. In contrast, in the output layer, the multiplication processing is used as an activation function free of the output weights which aims to reduce the number of tunable parameters. The output layer with three neurons represents the adaptive PID controller parameters. In this work, the tunable parameters are updated using the Lyapunov stability criterion to guarantee optimization and learning stability. In addition, the plant identification based on the Wiener-type model and quantum neural network is introduced to estimate the sensitivity of the plant output to the control signal. The proposed neural network structure yields only 30 tunable parameters for the proposed controller and 13 tunable parameters for the proposed identifier while the con-ventional neural network structure in other works requires 90 tunable parameters just for the controller. To evaluate the performance of the proposed APIDC-QNN, it is practically operated on a non-holonomic two-wheel mobile robot (NHWMR). Moreover, to investigate the robustness, superiority, and convergence speed of the proposed APIDC-QNN, some experimental tasks are discussed. The simulation and practical results show the powerful processing and the superiority of the proposed APIDC-QNN over that designed based on conventional neural networks by recording the minimum values of the performance indices.

This study introduces a general type-2 Takagi–Sugeno–Kang fuzzy controller (GT2-TSKFC) for controlling uncertain systems. The proposed GT2-TSKFC uses equidistant type-2 triangular membership functions (MFs) for the antecedents, Larsen's implication method, type-1 fuzzy sets for the consequent parameters, and a direct defuzzification method. The analytical structure of the proposed controller indicates that the alpha-plane and apex of the secondary MFs have a noticeable effect on calculating the control signal. Adaptation of the alpha-plane and apex of the secondary MFs is performed using the Lyapunov function to achieve the stability of the controlled system. The proposed controller is applied to an uncertain nonlinear inverted pendulum system. The results of the proposed control algorithm are compared with those of a general type-2 fuzzy controller with a specific number of alpha-planes, a quasi type-2 fuzzy controller, an interval type-2 fuzzy controller, and a type-1 fuzzy controller to demonstrate the robustness and effectiveness of the proposed scheme.

This paper introduces a novel structure of a polynomial weighted output recurrent neural network (PWORNN) for designing an adaptive proportional—integral—derivative (PID) controller. The proposed adaptive PID controller structure based on a polynomial weighted output recurrent neural network (APID-PWORNN) is introduced. In this structure, the number of tunable parameters for the PWORNN only depends on the number of hidden neurons and it is independent of the number of external inputs. The proposed structure of the PWORNN aims to reduce the number of tunable parameters, which reflects on the reduction of the computation time of the proposed algorithm. To guarantee the stability, the optimization, and speed up the convergence of the tunable parameters, i.e., output weights, the proposed network is trained using Lyapunov stability criterion based on an adaptive learning rate. Moreover, by applying the proposed scheme to a nonlinear mathematical system and the heat exchanger system, the robustness of the proposed APID-PWORNN controller has been investigated in this paper and proven its superiority to deal with the nonlinear dynamical systems considering the system parameters uncertainties, disturbances, set-point change, and sensor measurement uncertainty.

In this paper, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is proposed for a class of nonlinear networked Wiener systems under packet dropout and time varying delay. The proposed AIT2FC compensates the negative effects of the packet dropout and time varying delay in both forward and feedback loops. The structure of the proposed AIT2FC has three parts, an adaptive interval type-2 Takagi-Sugeno (IT2TS) fuzzy controller, an IT2TS fuzzy Wiener model (IT2TS-FWM), and a time-varying delay and packet dropout compensator. The adaptive IT2TS fuzzy controller has a cascade connection; an IT2TS fuzzy controller followed by an inverse of an autoregressive moving average (IARMA) system. The nonlinear Wiener system is identified online by an IT2TS-FWM. An adaptive Smith predictor (ASP) is proposed to compensate the negative effects related to the time-varying delay. For each communication channel, the packet dropout is compensated via designing a compensation term in the stochastic Bernoulli approach. Based on the Lyapunov stability (LS) function, the parameters of the proposed AIT2FC are updated online. Also, the learning rates are updated online based on the LS function to avoid singularities and guarantee both the stability and fast convergence of the AIT2FC. The results conclude that the proposed controller is better than the other existing controllers. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

The objective of this study is to finding a solution to overcome the uncertainties and nonlinearities of a robot manipulator, like disturbances and other variations, in real-time by designing and implementing an adaptive 2-degree of freedom (DOF) proportional-integral-derivative (PID) controller. The proposed controller is performed using a supervisory fuzzy logic system (FLS). The employed controller proves its robustness in trajectory tracking. The performance analysis for PID, 2-DOF PID, and adaptive 2-DOF PID controllers is performed by doing the simulation using Matlab. On the other hand, the proposed controller is implemented practically using a microcontroller to prove that the proposed adaptive 2-DOF PID controller has a better performance compared to other traditional fixed-gain controllers. Also, the controller is more robust in the case of the presence of disturbances or other variations.

In the present paper, a hybrid deep learning diagonal recurrent neural network controller (HDL-DRNNC) is proposed for nonlinear systems. The proposed HDL-DRNNC structure consists of a diagonal recurrent neural network (DRNN), whose initial values can be obtained through deep learning (DL). The DL algorithm, which is used in this study, is a hybrid algorithm that is based on a self-organizing map of the Kohonen procedure and restricted Boltzmann machine. The updating weights of the DRNN of the proposed algorithm are developed using the Lyapunov stability criterion. In this concern, simulation tasks such as disturbance signals and parameter variations are performed on mathematical and physical systems to improve the performance and the robustness of the proposed controller. It is clear from the results that the performance of the proposed controller is better than other existent controllers.

Precise speed control combined with good transient performance and high efficiency makes Permanent Magnet synchronous motors (PMSM) an interesting solution for a wide range of industrial and technological applications. Different techniques have been developed for rotor position estimation in the direction of eliminating Mechanical or optical position sensors to achieve a better dynamic response and accurate control. This paper presents a new Flux linkage-based speed and position estimation technique that experimentally improves the performance of the PMSM sensor-less drive system. Complete design methodology with detailed design rules is provided. The proposed technique is verified through experimental results and implemented using Digital Signal Processor (DSP).

The purpose of the paper is to conduct an investigation and obtain a solution through designing a fuzzy proportional-derivative (PD) controller to overcome the uncertainties of a robot manipulator in real-time, like disturbances and other variations. The fuzzy logic controller plays a big rule especially in case of quantitative data shortage relating the input to output in industrial processes and...

This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an observer using feed-forward NN (FFNN) is performed to estimate the controlled system states. Second, an adaptive control based on actor-critic RL is developed, in which a quantum diagonal recurrent neural network (QDRNN) is proposed to represent the critic and actor parts. The critic QDRNN is applied to perform the “strategic” utility function, and it is minimized by the actor QDRNN. The proposed adaptive tracking NN control guarantees the faster convergence due to the developed updated algorithm for the controller parameters, which is derived using the Lyapunov function. Simulation and practical results indicate the robustness of the proposed observer-based adaptive control relative to other existing controllers.

In this paper, a fractional order sliding mode controller (FOSMC) is proposed for nonlinear coupled tank system. The proposed controller integrates the advantages of the fractional order control and sliding mode control. Fractional order controller has an extra flexibility to meet the specifications of the controller design and sliding mode control is a robust controller, which is able to respondtoexternal disturbances combined with nonlinear systems.The objective of the controller is to allow the system states to move to the sliding surface and remain on it so as to ensure the asymptotic stability of the closed-loop system. The stability analysis for the proposed controller is studied based on the Lyapunov stability theorem. Simulation results show the ability of the proposed controller to improve the system performance especially when the controlled system is exposed to external disturbances and variation of reference trajectory compared with the conventional proportionalintegral-derivative controller (PID) and conventional sliding mode controller (SMC), which is based on integer order derivatives. Keywords—Fractional control, Sliding mode control, Lyapunov function,Nonlinear systems.

An adaptive fractional order sliding mode controller (AFOSMC) is introduced in this study to control a nonlinear system. This controller includes two units. The primary unit is a fractional order sliding mode control unit which incorporates the sliding mode and the fractional order control features. Second unit is employed for online adjusting the parameters of fractional order sliding mode contro...