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Permanent magnet synchronous motors (PMSMs) occupy a core position in various industrial and household equipment due to their excellent energy efficiency, high-power density, and low-noise characteristics. However, facing dynamic factors such as load fluctuations, environmental temperature changes, and manufacturing tolerances, the stability and optimization of its operational efficiency have become a major challenge. To address these issues, this study innovatively proposes a multimodal adaptive control strategy based on embedded neural networks, aiming to enhance the adaptability of PMSM to complex working environments through intelligent learning and optimized decision-making. The core of the research is to construct a control system based on embedded neural networks, which can capture the characteristics of PMSM in different operating modes in real time and learn the corresponding optimal control strategy. The advantages of embedded neural networks lie in their compact model design and efficient computing power, making them highly suitable for resource-constrained motor control systems. Through multimodal learning, the system can recognize and adapt to various states of motor operation, such as start-up, acceleration, steady-state operation, and deceleration, thereby achieving precise control of motor performance.

Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.

The predefined performance tracking control issue of a vertical take-off and landing (VTOL) aircraft system is discussed in this work. Employing the presented control performance function, the constrained error signal of the aircraft is switched into an equivalent unconstrained signal. Moreover, fuzzy logic systems are introduced to address unknown nonlinear dynamics problems in aircraft system. Furthermore, in order to address the approximation errors and the external disturbances of aircraft system, parameter-adaptive update laws are proposed in the analysis process. Subsequently, adaptive fuzzy sliding-mode control laws are designed by combining fuzzy control method with sliding-mode control technique. By applying the redesigned sliding-mode functions and the Lyapunov stability method, the output of the VTOL aircraft system can track the desired trajectory, and the tracking errors are constrained in a predefined range. Finally, the availability of the proposed control laws is testified through simulation examples.

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H_∞ control scheme.

The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.

We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.

This paper addresses the synchronization problem of two coupled dynamos systems in the presence of unknown system parameters. Based on Lyapunov stability theory, an active control law is derived and activated to achieve the state synchronization of two identical coupled dynamos systems. By using Gerschgorin theorem, a simple generic criterion is derived for global synchronization of two coupled dynamos systems with a unidirectional linear error feedback coupling. This simple criterion is applicable to a large class of chaotic systems, where only a few algebraic inequalities are involved. Numerical simulations results are used to demonstrate the effectiveness of the proposed control methods.

This paper investigates adaptive generalized projective synchronization (GPS) between two novel hyperchaotic systems with different structure and fully uncertain parameters. Based on the Lyapunov stability theorem and the adaptive control theory, GPS between the two hyperchaotic systems is achieved by proposing a new adaptive controller and a novel parameters estimation update law. Strict theoretical proof is put forward. Numerical simulations are presented to demonstrate the effectiveness of the proposed GPS scheme and verify the theoretical results.

Cluster modified projective synchronization of two community networks with nonidentical nodes is studied in this paper. Each network has a unique dynamics and its clusters have different parameter sets which could make their dynamics chaotic or periodic for instance. Therefore, we are dealing with varieties of dynamics in these clusters. By introducing an adaptive control gain in our controller design and using Lyapunov stability theory, we show that two community networks can reach to the synchronized state having arbitrary matrix scaling factor between corresponding nodes of the networks. Moreover, using this matrix we can observe different synchronization regimes simultaneously in each pair of corresponding nodes.

Cluster modified projective synchronization (CMPS) between two topologically distinct community networks is studied in this paper. Each cluster here has a unique dynamics at least with respect to the parameter sets. Using an adaptive feedback control gain and a matrix scaling factor, we show that CMPS between two community networks can be realized with considering minimum assumptions and imposing just few restrictions on the configuration set. We use Lyapunov stability theory for the proof and employ computer simulation to confirm our result on randomly generated community networks. Simulations also show the possibility of having hybrid synchronization between the two networks.

The exponential synchronization and anti-synchronization of nonautonomous chaotic systems with uncertain parameters are studied. The adaptive controller is designed and analytic expression of the controller and the adaptive laws of parameters are given. Based on the Lyapunov stability theory, the exponential stability of the error system is proved. Numerical simulations of two nonautonomous chaotic systems with uncertain parameters are presented to illustrate the ability and effectiveness of the proposed method.

In this paper, cluster synchronization for fractional-order complex network with nondelay and delay coupling is investigated. Based on the stability theory of fractional-order systems and the properties of fractional derivative, both static and adaptive control schemes are adopted to design effective controllers. Sufficient condition for achieving cluster synchronization about static controllers is provided. From the condition, the needed feedback gains can be estimated by simple calculations. Further, adaptive control scheme is introduced to design unified controllers. Noticeably, in the adaptive controllers, the feedback gains need not be calculated in advance and can adjust themselves to the needed values according to updating laws. Finally, numerical simulations are given to demonstrate the correctness of the obtained results.

A modified adaptive control scheme for synchronization of an uncertain Lorenz hyperchaotic system is proposed. Based on the Lyapunov stability theory, the sufficient condition for the synchronization is analyzed and proved theoretically. With the condition derived, parameter identification and synchronization of the Lorenz hyperchaotic system with all the unknown system parameters can be achieved simultaneously. Numerical simulations are presented to illustrate the effectiveness of the proposed synchronization scheme.

Adaptive controllers are designed to synchronize two different chaotic systems with uncertainties, including unknown parameters, internal and external perturbations. Lyapunov stability theory is applied to prove that under some conditions the drive-response systems can achieve synchronization with uniform ultimate bound even though the bounds of uncertainties are not known exactly in advance. The designed controllers contain only feedback terms and partial nonlinear terms of the systems, and they are easy to implement in practice. The Lorenz system and Chen system are chosen as the illustrative example to verify the validity of the proposed method. Simulation results also show that the present control has good robustness against different kinds of disturbances.

In this paper, a generalized (lag, anticipated and complete) projective synchronization for a general class of chaotic systems is defined. A systematic, powerful and concrete scheme is developed to investigate the generalized (lag, anticipated and complete) projective synchronization between the drive system and response system based on the adaptive control method and feedback control approach. The hyperchaotic Chen system and hyperchaotic Lorenz system are chosen to illustrate the proposed scheme. Numerical simulations are provided to show the effectiveness of the proposed schemes. In addition, the scheme can also be extended to research generalized (lag, anticipated and complete) projective synchronization between nonidentical discrete-time chaotic systems.

In this paper, a chaotic synchronization scheme is proposed to achieve adaptive synchronization between a novel hyperchaotic system and the hyperchaotic Chen system with fully unknown parameters. Based on the Lyapunov stability theory, an adaptive controller and parameter updating law are presented to synchronize the above two hyperchaotic systems. The corresponding theoretical proof is given and numerical simulations are presented to verify the effectiveness of the proposed scheme.

This paper further investigates the adaptive full state hybrid projective synchronization (FSHPS) of hyper-chaotic systems — CYQY system with fully unknown parameters and perturbations. Based on the Lyapunov stability theory, adaptive controllers and updating laws of parameters can be designed for achieving the FSHPS of the CYQY hyper-chaotic systems with the same and different structures. Two groups numerical simulations are provided to verify the effectiveness of the proposed scheme.

A variety of electric components can be used to bridge connection to the nonlinear circuits, and continuous pumping and consumption of energy are critical for voltage balance between the output end. The realization and stability of synchronization are mainly dependent on the physical properties of coupling channel, which can be built by using different electric components such as resistor, capacitor, induction coil and even memristor. In this paper, a memristive nonlinear circuit developed from Chua circuit is presented for investigation of synchronization, and capacitor, induction coil are jointed with resistor for building artificial synapse which connects one output of two identical memristive circuits. The capacitance and inductance of the coupling channel are carefully adjusted with slight step increase to estimate the threshold of coupling intensity supporting complete synchronization. As a result, the saturation gain method applied to realize the synchronization between chaotic circuits and physical mechanism is presented.

This paper studies the adaptive full state hybrid projective synchronization method. Based on the Lyapunov stability theory, an adaptive controller is designed. It is proved theoretically that the controller can make the states of the dynamical system and the response system with known or unknown parameters asymptotically full state hybrid projective synchronized. A unified chaotic system is used as an example and numerical simulations show the effectiveness of the scheme.