This paper proposes an innovative Energy-Efficiency-Aware Fixed-Time (EEAFT) control strategy for distributed permanent-magnet synchronous motor systems. The primary contribution is achieving rapid, precise speed consensus while effectively balancing energy consumption against consensus error, a challenge often overlooked in fixed-time (FxT) control designs. A key novelty lies in the introduction of a real-time reward mechanism, uniquely derived from low-pass filtering both the system's consensus error and the control input signals. This adaptive reward function dynamically adjusts the upper bound on convergence time. When the convergence time becomes excessively stringent, a substantial increase in control effort is required. This can lead to energy spikes and hardware overload. To mitigate these risks, the bound on convergence time is then relaxed. Conversely, when large consensus errors necessitate an accelerated response, the bound on convergence time is tightened to expedite error correction. To enhance robustness against unmodeled friction, winding losses, and external disturbances, a fuzzy logic system is employed to approximate unknown nonlinear dynamics, while a disturbance observer compensates for residual errors. Rigorous theoretical analysis confirms the boundedness of all system signals and guarantees FxT convergence under the proposed EEAFT framework. Extensive experimental results demonstrate the effectiveness and superiority of the EEAFT-based controller, showcasing high-precision speed synchronization across multiple motors and achieving an improved trade-off between rapid transient response and overall energy efficiency.

Traditional rigid-time control in multimachine power systems faces severe actuator saturation risks due to the conflict between strict stability demands and finite generator capacities. To address this challenge, this paper presents a novel distributed control strategy for auxiliary voltage regulation, establishing a control chain comprising a modeling foundation, a core coordination mechanism, and an implementation guarantee. For the modeling foundation, a nondimensional analysis framework is developed to provide a unified structural basis across heterogeneous generators. This framework reveals intrinsic multimachine physical scaling laws, facilitating the active regulation of effective constraint boundaries through base quantity adjustment. Based on this structural foundation, an input-time coordination mechanism functions as the core autonomous saturation mitigation mechanism, which dynamically extends the convergence deadline in response to surging energy demands to relieve overloads. Finally, regarding the implementation guarantee, a distributed adaptive controller is synthesized, where a fuzzy logic system is employed to approximate system nonlinearities, and a singularity-free prescribed-time function is introduced to ensure initial feasibility and eliminate infinite gain risks. For the studied multimachine scenarios, the performance of the proposed scheme is evaluated based on numerical simulations, demonstrating that this synergistic framework significantly alleviates the practical control burden while preserving transient stability.

This paper proposes an all agents connectivity-preserving method and a data-filter memory-based event-triggered (ET) strategy to design cooperative learning control algorithm. Firstly, a type of error functions are proposed to achieve that all agents are within the communication boundary, which do not limit the initial values of agents. The designed method can dynamically adjust the boundary function based on the initial position of agents and gradually converge to the preset communication boundary, without abandoning any agents. Secondly, a data-filter memory-based ET strategy is proposed, which includes the designed error-based data filtering rules. The filtering rules avoid the problem that the ET mechanism stores abnormal historical data when the system has faults. Moreover, the presented error-based cooperative learning adaptive protocol does not need to presuppose that neighbor weights are bounded, reducing the conservatism. Finally, based on the above works, the constructed ET control method can achieve control objectives, and the effectiveness is demonstrated through theoretical analysis and simulation results. Note to Practitioners—In practice, multiagent systems (MASs) communicate through wireless communication mostly, which inevitably leads to an upper limit on the communication distance between agents. Exceeding the limitation of communication module will cause the problem that MASs cannot achieve signal transmission. Therefore, it is crucial to design appropriate constraint methods for different initial positions of agents to ensure that all agents can enter the predetermined communication boundary. In addition, the memory ET strategy can calculate the threshold of conditions based on the historical data. But in practice, it cannot guarantee the continuous normal operation of the system. If there are abnormal values in the stored signal data, this will lead to unreasonable calculation of the threshold. In response to this issue, this paper considers additional data filtering rules to avoid storing data when systems exist faults. Meanwhile, the cooperative learning algorithm proposed in this paper can adjust the learning information weights based on the control performance of neighbor agents, and remove the assumption of bounded neighbor learning laws.

This paper investigate the input constraint and signal encryption for distributed control. Firstly, for the algorithm operation session, an additional information protection mechanism is designed, and the designed encryption algorithm can ensure that both the encrypted signal and the key signal have chaotic randomness. The mask signal is composed of multiple chaotic states, which has higher randomness. The key signal is also encrypted, effectively improving the security of the encryption algorithm. Even when the channel of key is not secure, information security can still be guaranteed. Moreover, information decryption only requires an integral algorithm and does not require an embedded decryption module that satisfies chaotic synchronization conditions, effectively reducing the complexity of decryption. Secondly, the proposed output constraint function can dynamically adjust the constraint boundaries based on control performance, which avoids the problem that the boundary is too strict to affect the control performance. At the same time, the boundary initial value is related to the output initial value, which effectively improves the convenience of algorithm migration and avoids the need to consider designing different parameters in different application environments. Based on the above two main works, the designed control algorithm can meet the considered control objectives using Lyapunov theory, and the simulation results also verify the effectiveness of proposed methods.

This paper proposes some novel algorithms for photovoltaic (PV) energy DC circuit conversion systems to extract maximum power under the constraint of information security. First, a discrete-degree-judging-based composite chaotic mask function generation (DCCG) algorithm is constructed. This algorithm utilizes all the states in the chaotic system to construct a more volatile chaotic signal, effectively increasing the complexity of the mask signal and enhancing the encryption of the photovoltaic energy system. Secondly, for the superposition process of the encrypted signal and the mask signal, a Gaussian-high-dimensional mapping-based adaptive weight calculation (GMAWC) method is designed to adaptively adjust the superposition weights. This adaptive adjustment helps avoid issues of insufficient encryption and excessive noise interference caused by the large value domain variation between the mask signal and the system states. Moreover, an upper bound on the decryption bias tolerated by the PV energy conversion system is explored, ensuring that the algorithmic framework has greater decryption bias tolerance while achieving maximum power extraction. Finally, both theoretical analysis and data results show that the proposed encryption-decryption-control framework has better security, applicability and decryption bias tolerance.

For the superheated steam temperature control system, an optimized disturbance observer and output-constrained control algorithm have been designed. Initially, the original system with output constraints is transformed into a system without any state constraints, suitable for backstepping design, through state transformation. Then, based on the concept of negative gradient optimization, a gain iterative disturbance observer is constructed, which dynamically improves the control accuracy of the system compared to a constant gain disturbance observer. Finally, an adaptive neural control scheme based on the gain iterative disturbance observer is proposed, proving that all output states are constrained within predefined bounds, and all closed-loop signals are semi-globally uniformly bounded. The effectiveness of the proposed scheme is demonstrated through a simulation example of the superheated steam temperature system.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step $n$ and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

In this research, an event-based adaptive fixed-time fuzzy containment fault-tolerant control issue for multi-agent systems (MASs) with positive odd rational powers is discussed. As a result of the presence of unknown nonlinear functions, fuzzy logic systems (FLSs) can be employed to estimate them with the support of FLSs’ approximation capability. The adding a power integrator (API) can be introduced to handle the difficulties encountered in the backstepping design process for high-order nonlinear systems. Further, the event-triggered control ideology was carried out among neighbors of MASs, which is used to minimize the communication burden. In addition, the controlled MASs considered four types of sensor faults, adopting the adaptive control methods to achieve effective estimation of unknown fault parameters. According to the frame of backstepping control, an adaptive fuzzy containment event-triggered controller for MASs with positive odd rational powers was designed that can realize all signals are fixed-time bounded, even if MASs may exist sensor faults. In the last resort, the illustrative example can manifest the validity of the suggested approach.