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This article proposes an extreme learning machine (ELM)‐based super‐twisting integral terminal sliding mode control (STITSMC) for speed regulation of a permanent magnet synchronous motor (PMSM). First, the PMSM is modeled in a non‐cascade control structure for fast system response and uncertainty compensation in the speed and torque loops. Second, the STITSMC is designed with integral actions in both the sliding surface and the reaching law to reduce chattering. Third, the ELM is constructed to compensate for the system lumped disturbance, and relax the disturbance upper bound required by the controller which further reduces the chattering. Fourth, the stability of the whole control system is proved based on the Lyapunov method and the finite time convergence regions are derived for both the reaching and the sliding phases. Finally, the comparative simulations and experiments are conducted to show the superiority of the proposed control. This article proposes an extreme learning machine (ELM)‐based super‐twisting integral terminal sliding mode control (STITSMC) for speed regulation of a permanent magnet synchronous motor (PMSM). First, the PMSM is modeled in a non‐cascade control structure for fast system response and uncertainty compensation in the speed and torque loops. Second, the STITSMC is designed with integral actions in both the sliding surface and the reaching law to reduce chattering. Third, the ELM is constructed to compensate for the system lumped disturbance, and relax the disturbance upper bound required by the controller which further reduces the chattering. Fourth, the stability of the whole control system is proved based on the Lyapunov method and the finite time convergence regions are derived for both the reaching and the sliding phases. Finally, the comparative simulations and experiments are conducted to show the superiority of the proposed control.

Controlling the Permanent Magnet Synchronous Motor (PMSM) can be challenging due to the nonlinearity of its dynamics, which makes it difficult to design control strategies that are both robust and effective. To address this challenge, this paper presents a novel control strategy rooted in the concept of passivity that combines field‐oriented control (FOC). This strategy compels the PMSM to accurately follow velocity and electrical torque trajectories. The approach, known as passivity‐based control (PBC), entails reshaping the inherent system energy while introducing the necessary damping to attain the desired objectives. A crucial aspect involves identifying workless force terms within the process model. Despite their presence, these terms do not impact the energy balance and stability properties. As a result, eliminating these terms is unnecessary. This simplicity in control architecture not only preserves system stability but also bolsters overall robustness. The system's overall stability and the current tracking error's exponential convergence have both been demonstrated analytically. In order to maintain stability, the controller accounts for the nonlinearities of the plant and approximates the unstructured dynamics of the PMSM. The proposed control is designed using the dq model of the PMSM, which avoids the model's structure destruction due to singularities, since the dq model does not depend explicitly on the rotor angular position. Experimental results shown further, illustrate speed and position control with a desired pair calculated by a filter or a proportional‐integral (PI) controller for speed control and a proportional‐integral‐derivative (PID) controller for position control. Also the correlation between practical and theoretical results is given as well as the robustness test in relation to the uncertainties of the PMSM's inertia moment. The results demonstrates the effectiveness of the proposed strategy in controlling the PMSM under different operating conditions, highlighting its potential for industrial applications. The main advantage of this approach is that it does not cancel out the nonlinear features of the system, but rather compensates for them in a damped manner. This allows for a more effective and robust control strategy.

The levitation control system is a critical subsystem in maglev trains, ensuring stable levitation of the train on the guideway. Achieving stable levitation requires providing the system with accurate levitation gap and corresponding velocity data while minimizing phase lag. This work proposes an enhanced tracking differentiator (TD) to reduce phase lag in both filtering and differentiation of input signals with varying noise levels. The improved performance is achieved by incorporating system damping and an amplitude factor into the control algorithm used to design the differentiator. Theoretical analysis guarantees the convergence of the proposed algorithm. Simulations and experiments conducted on the levitation gap data demonstrate the superior performance of the proposed TD in reducing phase lag for both filtered and differentiated signals. Furthermore, experimental results highlight the improved performance of the feedback controller when employing the proposed TD. This work proposes an enhanced tracking differentiator to ensure a small phase lag in both filtering and differential signals, The favourable characteristic is obtained by including the system damping and the amplitude factor into the control algorithm that is applied to construct the differentiator.

Control systems rely heavily on the accuracy and reliability of sensor data; however, the integrity of these data can be compromised through spoofing attacks, leading to significant modelling errors that can render control impractical. In addition, centralized control poses a significant threat to system security. To address these issues, a distributed framework is suggested for a discrete‐time nonlinear system that encounters unknown dead‐zones at its input. The framework uses the inherent resilience of a decentralized peer‐to‐peer network to secure information exchange, eliminating the need for prior knowledge of system dynamics or potential attacks. The proposed framework performs two complex tasks: identifying the nonlinear system and dealing with the unknown nonlinearity at the input in the form of a dead‐zone. An adaptive dead‐zone inverse is used to handle the unknown nonlinearity at the input in the form of a dead‐zone and integrate blockchain technology to secure communication between components. The blockchain component ensures tamper‐proof data transmission and resistance to cyberattacks, providing both detection and defence mechanisms without prior knowledge of system dynamics or potential attacks. The actuator and plant components are matched and synchronized using a private network with static nodes, ensuring deterministic and well‐coordinated communication. Simulation results demonstrate that the proposed framework both with and without blockchain integration, maintains stability and outperforms traditional methods in terms of robustness and accuracy, even when all parts of the framework are adjusted in response to attacks. In response to sensor data integrity challenges and security risks in centralized control systems, a distributed framework is proposed for discrete‐time nonlinear systems with unknown dead‐zones at the input. Leveraging decentralized peer‐to‐peer networks, this framework enhances security by securing information exchange without prior knowledge of system dynamics. The framework addresses system identification and the management of input dead‐zones using an adaptive dead‐zone inverse, ensuring stability even under attack scenarios through simulation results and matched actuator‐plant components in a private network with static nodes.

PurposeThis paper aims on the trajectory tracking of the developed six wheel-legged robot with heavy load conditions under uncertain physical interaction. The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal friction, external environment interaction, should be considered in the practical robot system.Design/methodology/approachIn this paper, a fuzzy approximation-based model predictive tracking scheme (FMPC) for reliable tracking control is developed to the six wheel-legged robot, in which the fuzzy logic approximation is applied to estimate the uncertain physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the electric six wheel-legged robot (BIT-NAZA) is presented.FindingsCo-simulation and comparative experimental results using the BIT-NAZA robot derived from the developed hybrid control scheme indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability.Originality/valueThis research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities and facilitate the control performance of the mobile robots in a practical system.

In this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme. In this paper, a novel discrete‐time extended state observer‐based model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL).

This paper studies the event triggered delayed impulsive control strategy for quasi‐synchronization for complex dynamical systems with parameter mismatch and packet loss. By constructing a comparative lemma with time delays and Lyapunov function, a few sufficient synchronization conditions are proposed by Cauchy matrix with time delays to analyze the system. Meanwhile, the possibility of packet loss is also considered and the quasi‐synchronization of the system is investigated by using both event triggered delayed impulsive control and self‐triggered impulsive control mechanisms. Theoretical analysis results are validated by numerical simulations.

This study concerns with robust tracking control problem for the longitudinal model of airbreathing hypersonic vehicles (AHVs). The AHVs include serious non-linearities, strong couplings, parametric perturbations and mismatched disturbances, which results in great difficultly in the controller design. By using back-stepping method and non-linear disturbance observer technique, a novel composite controller is proposed, which can guarantee system outputs asymptotically track their reference signals. A new idea is that disturbance estimations are introduced into virtual control law in each step to compensate the mismatched disturbances. As compared with other robust flight control methods for AHVs, the developed method exhibits not only excellent robustness and disturbance rejection performance but also the property of nominal performance recovery. Finally, the effectiveness of the proposed method is demonstrated by simulation results.

This study addresses the adaptive tracking control problem for a class of time-delay systems in strict-feedback form with unknown control gains and uncertain actuator non-linearity. The actuator non-linearity can be either backlash or dead zone, and the proposed approach does not require the knowledge of the bounds of non-linearity parameters. By applying an appropriate Lyapunov–Krasovskii functional and utilising the property of the well-defined trigonometric functions, the problems of time delay and controller singularity are avoided. The feasibility of using a static neural network to attenuate the effect of actuator non-linearity is proved with the aid of intermediate value theorem. Furthermore, it is proved that all closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the designed method.

The complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks. This article introduces a novel method, TDU‐MPC, for controlling firing patterns in neuronal networks. Through extensive numerical simulations, its efficacy in achieving desired firing patterns is validated, offering promising implications for brain–machine interfaces and therapeutic interventions for neurological disorders.