This work addresses the ultimate boundedness control problem for a class of cyber-physical switched systems under average dwell time (ADT) switching, where both the Try-Once-Discard (TOD) protocol scheduling and the uniform quantisation effects happen on the communication network between the sensor node and the controller. Under the regulation of the TOD protocol, only one sensor node at each instant can access the network and transmit data. First, a nonlinear-observer-based switching controller is designed for the cyber-physical nonlinear switched system to ensure that the closed-loop system is ultimately bounded, and the controlled output is locally minimised in the presence of the TOD protocol and uniform quantisation effects. Then a TOD protocol-dependent convex Lyapunov function is first proposed, and the ultimate boundedness conditions of the closed-loop system in mean square are derived by the Lyapunov function. Furthermore, the controller gains are obtained by solving a convex optimisation problem. Finally, a numerical example is given to illustrate that the TOD protocol mechanism is used to eliminate network transmission congestion at the expense of some performance of the switched system by comparing the cases with and without the TOD protocol.

Team theory analyzes cooperative decision-making among multiple agents that share a common objective while possessing different information. In team problems with nonclassical information structures, the classical separation collapses and prevents the formulation of a classical dynamic programming (DP) decomposition. This article focuses on a two-member dynamic team. We show how to construct information states that are independent of control strategies for both a centralised manager and individual members, which allows us to formulate a classical DP over the space of the decisions, recovering a separation-like architecture in decentralised settings.

This paper examines the finite-time incremental ( Q , S , R ) -dissipativity and finite-time incremental stability of switching nonlinear systems. First, we introduce the concept of finite-time incremental ( Q , S , R ) -dissipativity, which requires each active subsystem to be finite-time incremental ( Q , S , R ) -dissipative during its active period and allows the incremental storage function to increase at switching times. Second, by using state information, we design a switching law to achieve finite-time incremental ( Q , S , R ) -dissipativity, generalising the ‘min’ switching law. Third, multiple-incremental storage functions method is employed to obtain the finite-time incremental stability, even if none of the subsystems are finite-time incrementally ( Q , S , R ) -dissipative. Finally, to validate our findings, two examples are provided.

The present investigation deals with a finite capacity Markovian queue with various control policies for the optimal management of the queue served by a single server. Controlling arrival of the customers in the queues for traffic management and effective services is the most crucial and significant task of any service provider. Many control policies can be embedded in any service providing zone for better handling of queues. The server is unreliable and may breakdown while servicing the customers. To continue the service process, the failed server is sent for repair following special repair control policy known as threshold recovery (or delayed repair). The current study also involves two other control policies namely N-policy and F-policy that control the service initiation and arrival of the customers in the system respectively. The three policies (tri-control policies) namely threshold recovery policy, N-policy and F-policy are used simultaneously to study the model. The numerical approach has been used to study the transient behaviour of the model. Various performance measures categorically queueing measures and reliability measures are obtained. The cost function has also been developed to design a cost-effective queueing system under different policies. An application of the concerned queueing model to waste management process has also been discussed.

This paper investigates the practical fixed-time (PFxT) synchronisation of memristor-based bidirectional associative memory neural networks (MB-BAM-NNs) with event-triggered control (ETC) under network attacks. First, the parameter mismatch caused by the switching characteristics of memristor is addressed via convex analysis technique. Then, hyperbolic sine function based fixed-time (FxT) controller is proposed to suppress the unnecessary chattering. Also, to reduce communication load, an ETC strategy is adopted for both layers of the network during denial-of-service (DoS) attacks. Furthermore, some sufficient conditions for achieving PFxT synchronisation are established by constructing a suitable Lyapunov function and leveraging inequality techniques. Ultimately, a numerical simulation example is employed to verify the effectiveness of the theoretical analysis.

This article developed a reinforcement learning (RL) scheme to obtain the management strategy for microgrids with a novel discrete adaptive law which is facilitated to learn the critic network weight. The primary objective is to extend the battery's lifespan while reducing the microgrid cost. First, the system model and cost function are defined with the battery and load power. Then an iterative RL algorithm incorporating the new adaptive law is proposed to learn optimal microgrid control. To support this, both action and critic networks are implemented using neural network (NN) approximators. This paper also thoroughly examines the convergence behaviour of the NN and the stability of the overall control system. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.

There are some tuning techniques for sliding mode controls based on perturbation observers, on neuro-fuzzy systems and on discontinuous adaptive gains. It should be interesting to propose an alternative tuning technique not based on perturbation observers, neuro-fuzzy systems or discontinuous adaptive gains. This alternative could be the tuning technique utilising the adaptive closed-loop transfer function, which is commonly applied in proportional integral derivative (PID) controls with satisfactory results. This work aims to apply the tuning technique utilising the adaptive closed-loop transfer function in two continuous sliding mode controls called first order sliding mode control and second order sliding mode control, where this tuning technique uses continuous time functions in the transfer functions. Particularly, this work addresses how to apply the continuous sliding mode controls for stabilisation of a rotary inverted pendulum with perturbation.

Establishing the fractional stochastic Schrödinger evolution (FSSEs) equations in a Hilbert space with a complex symmetry and Poisson jumps is the primary goal of this work. Stochastic analysis, Mönch fixed point theorem (FPT), fractional calculus and semigroup theory all support the possibility of a moderate solution for the suggested system. We construct and prove the criteria for the mild solution's exponential fall. The trajectory (T-) controllability for the fractional Schrödinger equation is established in the latter portion, extending the findings of Durga & Muthukumar (2023) [Exponential behaviour of nonlinear fractional Schrödinger evolution equation with complex potential and Poisson jumps. Journal of Theoretical Probability, 36, 1–17.]. An example is given to verify the theoretical findings gained. The numerical simulation justifies the theoretical study.

This paper investigates the event-triggered optimal consensus control problem for Takagi–Sugeno (T–S) fuzzy multi-agent systems (MASs). In optimal consensus control design, taking each agent's control policy and its neighbours' control policy as rival players, a fuzzy optimal consensus control scheme is first developed by using differential graphical game theory. To save communication resources and actuator update times, an event-triggered mechanism is established and subsequently forms a fuzzy event-triggered optimal consensus control policy. To solve the event-triggered optimal consensus control problem, a model-free policy iteration (PI) learning algorithm is proposed to obtain the approximate solutions of game algebraic Reccati equations (GAREs). It is proved that the designed event-triggered optimal consensus control scheme not only ensure the T–S fuzzy MASs to achieve consensus control objective and also reach the Nash equilibrium. A simulation example is provided to illustrate the effectiveness of the proposed fuzzy event-triggered optimal consensus control method and theoretical results.

Given the non-linear property of the belief rule-base (BRB) approach, attribute weights (AW), rule weights (RW), and belief degrees of rule consequences (BDRC) should be carefully optimised in advance for a better performance. However, the impact of optimisation on these three types of parameters has not been carefully analysed. In this paper, we analyse the effect of optimisation on three types of parameters, beginning with those calculated by the belief-based method. Specifically, we adopt an archive-based differential evolution algorithm with three objective functions (namely, arithmetic mean accuracy (AMA), cross-entropy (CE), and mean absolute difference (MAD)) for optimisation. Then, we leverage the Wilcoxon signed-rank tests to analyse the benefits obtained from the optimisation. Experiments indicate that optimising these parameters is necessary, and the fitness function should be carefully designed to achieve better performance. Furthermore, CE is the most effective objective function for optimising RW. And, MAD outperforms others for BDRC.