This study considers to asymptotically stabilise a switched linear system which transmits its feedback signals through a digital communication network. Such network can only provide a finite bit rate to represent feedback signals. In the concerned switched linear system, the mode mismatch may occur between the sensor and the controller due to the unknown switching time instants. It assumes that the mode switching is slow enough in the sense of combined dwell time and average dwell time, each individual mode is stabilisable and the available feedback bit rate is high enough, but still finite. In order to save network bandwidth resources, novel event‐triggering strategies are implemented, i.e. only when pre‐defined events are triggered, the system samples and transmits feedback signals. Then under the proposed event‐triggering strategies, a stabilising bit rate condition for the concerned switched linear system is derived. Moreover, this study proves that the obtained stabilising bit rate under the proposed event‐triggering strategies can be strictly lower than the stabilising bit rate required by the earlier work based on periodic sampling. Simulations are done to illustrate the bit rate superiority of the proposed event‐triggering strategies.

This paper aims to stabilize a switched linear system which transmits its feedback information through digital communication networks. It investigates the effects of three factors on stabilization of the concerned system, including bounded network delay, unknown switching time instants and limited information to represent the system state. The network delay cannot be precisely known, may degrade the system’s performance and even destabilize it. Compared with the earlier work on delay-free switched linear systems, it needs to place stricter restrictions on the feedback data rate conditions and the average dwell time. It assumes that the switching is slow enough in the sense of combined dwell time and average dwell time, each individual mode is stabilizable and the available feedback data rate is high enough, but still finite. It proposes some encoding and control strategies to stabilize the switched linear systems with bounded network delay under some feedback data rate conditions.

A time-synchronized (TS) convergence control method for robotic manipulators is proposed. Adversely to finite-time control, a notion of time-synchronization convergence is introduced based on the ratio persistence property, which can ensure that all system components converge simultaneously in a finite time. Firstly, a robust disturbance observer is constructed to be compatible with the time-synchronized control framework and precisely estimate system uncertainties. Furthermore, we design a (finite) time-synchronized controller to ensure that all states of the robotic manipulator simultaneously converge to an equilibrium point, irrespective of initial conditions. Stability analysis shows the feasibility of the proposed TS control method. At last, simulations are performed with a two-link rehabilitation robotic system, and the comparison results indicate its superiority.

This article proposes a time-synchronized fault-tolerant convergence control method for n-degree-of-freedom robotic manipulators. The main challenge lies in driving the tracking errors of all joints to converge simultaneously, especially in the presence of system faults, external disturbances, and model uncertainties. We introduce a normalized sign function that guarantees the property of ratio persistence for all joints and plays a crucial role in time-synchronized convergence control. A time-synchronized convergence observer is proposed that not only adopts a time-synchronized convergence control framework but also overcomes the lumped uncertainty term, which includes the system fault components, external disturbances, and system uncertainties. A salient feature of this method is that, regardless of the initial state and various uncertainties, each component of the robot manipulator system can simultaneously converge to an equilibrium point. Simulations conducted on a two-link robotic manipulator demonstrate the notable benefits of the designed time-synchronized control method, as evidenced by the comparative results.

This article studies the feedback stabilization problem of an impulsive switched linear system whose feedback loop is closed over a digital network. Particularly, the combined effects of mode switches, impulses, quantization, network delay and external disturbances on the stability of that system are investigated. By extending the previous delay‐free and impulse‐free methods of reachable‐set approximation and propagation, some novel communication and control policies are designed to stabilize the concerned switched system with both network delay and impulses. In order to save the occupied network bandwidth, some event‐triggered control policies are proposed. To handle the effects of the mode switches and the impulse, we design event‐triggering conditions for both the case with no switch and no impulse on an inter‐event interval and the case with one switch or one impulse on an inter‐event interval. Note that the occurrence of a mode switch or an impulse leads to the switch of the event‐triggering conditions. Under the event‐triggered control policies, a stabilizing bit rate condition is derived. It is proven that even under impulses and network delay, the event‐triggered control policies can stabilize the switched system at a lower bit rate than earlier works based on conventional time‐triggered control policies.

The optimal control of the aluminum electrolysis production process (AEFP) presents an enduring challenge in industrial operations, largely due to its inherent dynamic nonlinearity, multivariable complexity, and susceptibility to significant disturbances. These factors hinder the development of precise predictive models for dynamic current efficiency. To address these problems, this article proposes a novel singular value decomposition unscented Kalman filtering neural network (NSVD-UKFNN) to improve the predictive accuracy of current efficiency in aluminum electrolysis. The approach begins by constructing a dynamic prediction model within the UKFNN framework, utilizing artificial neural networks (ANNs) to capture complex system behaviors. Singular value decomposition (SVD) is then incorporated into the unscented Kalman filtering neural network (UKFNN) architecture to compute the prior matrix square root, thereby enhancing model stability. Finally, the state variable prediction variance is reformulated as an objective function and optimized through gradient descent, effectively reducing error accumulation during computations. Comparative experimental results demonstrate that the proposed SVD-UKFNN significantly outperforms several related frameworks in predicting current efficiency, showcasing its potential for broader industrial applications.