Showing papers in "International Journal of Systems Science in 2023"

Event-triggered adaptive decentralised control of interconnected nonlinear systems with Bouc-Wen hysteresis input

Abstract: ABSTRACT This article presents an event-based adaptive decentralised output feedback control scheme for interconnected systems with Bouc-Wen hysteresis and unmeasured system states. To reduce some unnecessary data transmissions, a novel dynamic threshold adjustable event-triggering mechanism is proposed. In contrast with the traditional static threshold event-triggering mechanism, communication efficiency is greatly enhanced. Then, a neural networks-based observer is constructed to address the problem of unmeasured states, and the dynamic surface control method is used to address the ‘explosion of complexity’ that comes up when traditional backstepping design processes are used. Meanwhile, the Nussbaum function is introduced to eliminate the effect of unknown hysteresis. By resorting to the Lyapunov stability theory, it can be verified that all signals in the close-loop system are uniformly ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the developed control scheme.

Controller design on a new 2DOF PID structure for different processes having integrating nature for both the step and ramp type of signals

Abstract: Chemical reactors, heat exchangers, level-loops, and distillation columns are commonly encountered in the chemical process industry, and these are having a significant role in the functioning of any chemical process. For the smooth and efficient functioning of any such process, a robust control law design is an important work to be considered. In this paper, a modified 2DOF robust PID control law has been developed to ensure satisfactory performance in both the step and ramp type references and disturbances. Controller parameters are derived with Maximum sensitivity, Gain, and Phase margin specifications. Case studies with different chemical processes like CSTR, distillation column, heat exchangers, and boiler drum level-loop are included for both the nominal and perturbed cases. Lastly, a performance comparison of ISE (Integral of square error), ITAE (Integral time absolute error), IAE (Integral absolute error), and Total variation (TV) of the control signal is also included.

Prescribed-time zero-error active disturbance rejection control for uncertain wheeled mobile robots subject to skidding and slipping

Abstract: ABSTRACT This paper investigates the prescribed-time control problem for a class of wheeled mobile robot (WMR) subject to nonparametric skidding, slipping and input disturbance in an inner-outer loop framework. First, by backstepping approach, the virtual linear and angular velocities are acquired to follow the reference path to one special point. Then, different from the conventional ones for canonical integral cascade model, a novel prescribed-time extended state observer (PTESO) is designed for WMR such that the unknown skidding and slipping can be observed and compensated. By developing the active disturbance rejection control technique, the zero-error control is achieved for WMR in prescribed time instead of ultimately uniform boundedness. Moreover, the influence of the uncertainty is attenuated by the compensation scheme based on the PTESO estimation. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method.

Experimentally verified optimal bi-loop re-located IMC strategy for unstable and integrating systems with dead time

Abstract: Internal model controllers (IMCs) are popular strategies for controlling stable systems with dead time. The relocated IMC (RIMC) design reportedly achieves the expected performance-robustness tradeoff for stable processes. Hence, an attempt has been made in this work to modify the RIMC (as a bi-loop RIMC proportional-derivative (RIMC-PD)) strategy to make it applicable to a class of unstable and integrating systems involving dead time. The secondary-loop stabilisation is achieved with a PD controller constructed by Routh stability constraints. The primary loop contains a RIMC controller for reference following. Both primary, as well as secondary-loop controller parameters, are optimally tuned in the search space using the equilibrium optimiser subjected to minimal integral square error. The RIMC-PD control strategy delivers reasonable enhancement in performance measures when compared with some of the recently reported strategies. A robust stability investigation is also carried out. Finally, experimental verification of the RIMC-PD strategy is carried out using a magnetic levitation laboratory setup.

Test-based model-free adaptive iterative learning control with strong robustness

Abstract: ABSTRACT A test-based model-free adaptive iterative learning control algorithm (TB-MFAILC) with strong robustness is proposed in this paper. The algorithm improves the situation where existing model-free adaptive iterative learning control algorithms fail to converge or converge relatively slowly in noisy environments. Also, this work demonstrates the convergence and robustness of the proposed algorithm in different environments. Subsequently, the effectiveness of the proposed algorithm is illustrated by numerical comparison simulations with the existing model-free adaptive iterative learning control algorithm and the PD-based adaptive switching learning control algorithm in noisy environments. Finally, the advantages of the proposed algorithm are further illustrated through the analysis of relevant parameters.

International trade resilience with applied welfare economics: an analysis on personal protective equipment

Abstract: A global crisis such as a pandemic causes a decrease in the global trade of medical supplies. One of the most significant issues healthcare workers and people face is the shortage of personal protective equipment (PPE) items. This study constructs the first international trade model to link infectious disease dynamics and global trade networks, considering the important relationship between government preparedness, domestic manufacturers, and consumers. We examine social welfare measures here in the presence of quantity controls and taxes on the global trade flows. An equilibrium coverage among countries is investigated that integrates net government revenue, purchasing cost, transportation cost, and the health cost caused by the shortage of PPE supply. We develop an optimisation model that balances domestic firms and the global trade network to satisfy the total demand for each traded PPE product. The proportional change in value-added on domestic production is also studied by considering the marginal manufacturing costs of a face mask. The results obtained from testing our model show that the average quantity coverage by the global trade networks among four countries decreased by up to 28% using the proposed trade policy. Hence, a large amount of demand is met by relying on domestic production.

Stability analysis of T–S fuzzy systems with time-varying delay via parameter-dependent reciprocally convex inequality

Abstract: ABSTRACT The stability problem of T–S fuzzy systems with time-varying delay is investigated in this article. The purpose is to establish the less conservative stability conditions for T–S fuzzy systems with time-varying delays. Firstly, a parameter-dependent reciprocally convex inequality is proposed to improve the estimation accuracy of reciprocal convex terms. Secondly, based on the line-integral Lyapunov–Krasovskii (L–K) function and the developed parameter-dependent reciprocally convex inequality, a less conservative stability condition is established. Finally, two examples are used to verify the feasibility and superiority of the proposed method.

Stabilisation in distribution by delay feedback controls for hybrid stochastic delay differential equations

Abstract: This article aims to design a linear delay feedback control to stabilise an unstable hybrid stochastic delay differential equation in distribution. Under the global Lipschitz condition, sufficient criteria are established to guarantee the stability of the controlled system. Then LMI techniques are employed to design the control law in two structure forms: state feedback and output injection.

Non-fragile sliding mode observer based fault estimation for interval type-2 fuzzy singular fractional order systems

Abstract: In this paper, an interval type-2 fuzzy model is presented to describe the nonlinear singular fractional order systems (SFOSs) with . The problem of actuator and sensor fault estimation is addressed by designing a non-fragile sliding mode observer (SMO). Firstly, a new criterion is provided to analyse the admissibility of SFOSs, which does not need to divide the fractional order interval into interval and interval to study them separately. Then, by expanding the dimension of the SFOS, the non-fragile SMO and the sliding surface are constructed and the accurate estimations of faults and the SFOS state simultaneously are achieved. A new reaching law is designed, which can divide the process of the system moving to the sliding surface into three stages. Further the control law is constructed to guarantee the observation error reaches the sliding surface in finite time. Finally, three examples show the effectiveness of the proposed scheme.

Sampling-data-based distributed optimisation of second-order multi-agent systems with PI strategy

Abstract: ABSTRACT This paper investigates the optimisation problem of second-order multi-agent systems. Distributed optimisation algorithms are proposed based on sampling data. Two kinds of sampling techniques, namely, aperiodic sampling and dynamic event-triggered sampling, are utilised. Moreover, the proportional integral (PI) strategy is utilised in the proposed algorithms. Compared with the existing distributed optimisation algorithm based on periodic sampling, the proposed algorithm dependent on aperiodic sampling is more general. Compared with the existing steady event-triggered algorithm, the distributed optimisation algorithm based on dynamic event-triggered sampling has the merit of lower energy consumption. Under the assumption that the global cost function is strongly convex about global minimum point, it is proved that the proposed algorithms solve the optimisation problem. Lyapunov stability theory is applied to give sufficient criteria guaranteeing convergence to optimal point. Finally, the effectiveness of the proposed algorithms is illustrated by numerical simulation.

Joint optimisation of the maintenance and buffer stock policies considering back orders

Abstract: Inventory control and maintenance systems are two main processes in production systems that play a vital role in the efficiency of production resources. This study aims to propose a joint optimisation procedure for maintenance and inventory policies in a single-machine production system considering back orders. The failure rate is random and the minimum level of accessibility required for preventive maintenance is considered. To close the problem at hand to the real-world condition, demand is considered as uncertain and shortage is inevitable. In addition, the unsatisfied demand is considered as back order. The problem is described and formulated via a mathematical model and all the possible scenarios corresponding to the different possible cases of the problem are described. Then a numerical iterative procedure is introduced to solve the problem in a reasonable time. The proposed procedure does not explore all solution space, but it behaves like a numerical optimisation procedure and provides a near optimal solution. The performance of the proposed method is evaluated using a numerical instance based on a real case study. In addition, some sensitivity analyses are performed to identify the most important effective parameters.

LQR based optimal co-design for linear control systems with input and state constraints

Abstract: This paper considers a constrained co-design problem for linear time-invariant (LTI) systems. With a practical vision of real-world problems, constraints on the control signal and system states are involved in the proposed algorithm. The main goal is to design a sub-optimal controller by simultaneously obtaining the control policy and the model parameters. To this end, the conventional problem of solving the Hamiltonian-Jacobi-Bellman (HJB) equation is transformed into a nonlinear non-convex optimisation problem. Then, by reformulating the constraints of the optimisation problem, it is relaxed into a convex Semi-Definite Programming (SDP). By presenting an iterative method, the control cost, the performance, and the number of iterations are improved compared to conventional methods, and a closer result to the optimal solution is obtained. The performance and efficacy of the proposed algorithm are investigated through a case study on the physical load positioning system.

Fractional-order PID control of tipping in network congestion

Abstract: Tracing the rapid progress of communication network, the control of dynamic evolution of network has become a central issue. There are a lot of tipping phenomena in network congestion systems. Therefore, tipping control principally centres on traditional control policies, and some advanced control approaches need to be supplemented. In this paper, a fractional-order proportional-integral-derivative (PID) controller is introduced to a network congestion model to ponder corresponding bifurcation-induced tipping regulation. First, a fractional-order congestion model with fractional-order PID controller is constructed. Then the onset of the tipping induced by Hopf bifurcation of the uncontrolled model is studied. By contrast, the tipping point can be delayed under the controller for the controlled model. Some conditions under which Hopf bifurcation occurs are given. The stable and unstable ranges of control parameters for the controlled model are also deduced. At last, some simulated examples are given to verify the theoretical results and demonstrate the superiority of the controller in tipping regulation. Moreover, the bidirectional effects of the controller are displayed by manipulating the control parameters.

Analytical design of robust FO-PID controller for diffusion processes despite of uncertainty on all model parameters

Abstract: ABSTRACT Diffusion processes, as fundamental mechanisms for particle movement in systems with different concentrations, are used to describe many real-world physical, chemical, biological, engineering, economic and social phenomena. A diffusion process can be modelled via a fractional-order transfer function with time-delay, where its parameters may be affected by circumstance. Hereupon, this study proposes a pioneer robustness indicator to achieve the phase margin invariance regardless of concurrent uncertainty on different parameters of a diffusion process. Afterwards, an analytical procedure is suggested to tune a Fractional-Order Proportional-Integral-Derivative (FO-PID) controller for a diffusion process, to favourably regulate the values of gain crossover frequency and phase margin, such that the proposed robustness criterion is met. Moreover, the solvability of the problem is analytically investigated. Finally, a numerical simulation on robust temperature control during magnetic local hyperthermia, i.e. a common method to treat cancerous tumours, is presented to validate the efficiency of the paper achievements.

Impacts of sales mode and recycling mode on a closed-loop supply chain

Abstract: Statement of Removal Due to a technical issue, this article incorrectly published in the International Journal of Systems Science instead of the International Journal of Systems Science: Operations & Logistics. Therefore, this article has been removed from International Journal of Systems Science and republished in the International Journal of Systems Science:Operations & Logistics. The article is now available in the link: https://doi.org/10.1080/23302674.2023.2221076 Article title: Impacts of sales mode and recycling mode on a closed-loop supply chain Authors: N.Wan Journal: International Journal of Systems Science Version of Record Published Online: 06 April 2023 DOI: http://doi.org/10.1080/00207721.2023.2197434

Finite-time dissipative control design for one-sided Lipschitz nonlinear singular Caputo fractional order systems

Abstract: ABSTRACT This paper presents an efficient analytical approach for solving the problem of dissipative control in one-sided Lipschitz singular Caputo fractional-order systems subject to nonlinear perturbations. The approach is based on mathematical transformations, and fractional calculus. A state feedback controller is designed to ensure that the closed-loop system is regular, impulse-free, and finite-time bounded with a dissipativity performance index. The results are established using tractable strict linear matrix inequalities (LMIs) without any equality constraint. Two numerical examples are provided to demonstrate the effectiveness of the proposed approach.

On the controllability and observability of fractional proportional linear systems

Abstract: This paper deals with generalised Caputo fractional proportional linear time-invariant systems in a finite-dimensional space. The Laplace transformation method ensures the analytical solution of the desired fractional linear time-invariant systems. The present article presents the necessary and sufficient conditions for controllability and observability of the generalised Caputo proportional fractional linear time-invariant systems. These two properties can play a more fundamental role in system analysis before controller and observer designs are engaged. Moreover, we have acquired the criterion for generalised Caputo proportional fractional linear time-invariant systems as Kalman rank conditions. Some numerical examples are presented to show the applicability of the paper to demonstrate our findings. Finally, we derive the necessary and sufficient controllability conditions for the generalised Caputo proportional fractional-order nonlinear Chua's electric circuit.

Image-based prescribed performance visual servoing control of a QUAV with hysteresis quantised input

Abstract: ABSTRACT In this paper, a novel image-based prescribed performance visual servoing control scheme with hysteresis quantised input is proposed for quadrotor unmanned aerial vehicles (QUAVs, for short). First, based on the perspective projection principle, effective image features are defined on a image plane called virtual image plane, and the decoupled image feature dynamics are achieved with respect to target points. Then, the image feature dynamics and QUAV dynamics are combined to derive a nonlinear dynamic system, and the nonlinear system is decoupled into two subsystems based on the commonly used inner–outer loop control framework. Furthermore, the prescribed performance control-based visual servoing control scheme with hysteresis quantizer is designed, and the necessary stability proof is presented. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.

SMWO/D: a decomposition-based switching multi-objective whale optimiser for structural optimisation of Turbine disk in aero-engines

Abstract: ABSTRACT In this paper, a novel multidisciplinary design optimisation (MDO) algorithm is proposed, which is named as the decomposition-based switching multi-objective whale optimiser (SMWO/D). In particular, a penalty-Tchebycheff value-based decomposition framework is designed to decouple the strongly correlated conflicting objectives, so as to give comprehensive considerations to different disciplinary demands. To overcome the shortcoming of premature in the complicated multi-modal non-linear decision space, two adaptively switchable evolutionary modes are defined to enhance the ability of escaping from local optimum and promote a thorough global search with rich learning strategies. The proposed SMWO/D is evaluated on a series of benchmark functions, and the results show its competitiveness in terms of comprehensive performance as compared with other four popular decomposition-based multi-objective optimisation algorithms (MOAs). In addition, sensitivity analysis is carried out to determine the best parameter configuration of SMWO/D. Finally, in a case study of a real-world turbine disk structural optimisation, the practicality of the proposed SMWO/D is validated, which can effectively handle the multidisciplinary property of this complicated problem, thereby providing valuable experiences in the aero-engine MDO domain.

Non-fragile robust model predictive controller design for uncertain time-delay systems with input constraints

Abstract: ABSTRACT This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller.

Two networked predictive control methods for output tracking of networked systems with plant-model mismatch

Abstract: This paper addresses the output tracking control problem for networked systems with plant-model mismatch, where two-channel random network delays are considered. According to the different ways of obtaining future control commands, two networked predictive control (NPC) methods are designed. One is the NPC method using one-way time delays, called ONPC, and the other is the NPC method using round-trip time delays, called RNPC. Furthermore, they are compared in terms of the way of compensation for two-channel delays, prediction process, and communication load, respectively. Then, the closed-loop stability conditions of the RNPC system and the ONPC system for the plant-model mismatch and match cases are derived, and their output tracking performance is also theoretically analysed. Finally, the results of the theoretical analysis are verified through numerical simulation that illustrates the effectiveness of the proposed two NPC methods as well as their separate advantages.

Nonparametric identification based on Gaussian process regression for distributed parameter systems

Abstract: ABSTRACT This paper proposes a nonparametric identification method based on Gaussian process regression (GPR) for completely unknown nonlinear distributed parameter systems (DPSs). Inspired by linear parameter-varying (LPV) modelling approach, an interpolated spatio-temporal Volterra model is developed to represent the DPSs in nonparametric form, in which local Volterra models are interpreted as Gaussian processes. According to the empirical Bayesian approach, we design the third-order stable kernel structure used for embedding prior knowledge and derive the estimation of hyperparameters. The hyperparameters included in local weighting functions and kernel functions are determined by the maximum likelihood method. By utilising the nonparametric identification approach to avoid model structure selection, the proposed method can improve identification result for completely unknown distributed parameter systems. Finally, two case studies validate the effectiveness of the proposed identification method.

Hierarchical conditions for exponential stabilisation of time-varying delay systems using Bessel–Legendre inequalities and Finsler's lemma

Abstract: This paper addresses the exponential stabilisation of time-varying delay systems by means of Lyapunov–Krasovskii approaches, Bessel–Legendre inequalities and Linear Matrix Inequalities (LMIs). The key aspect of the proposed control synthesis method is that the LMI conditions are proved for the first time to be hierarchical under time-varying delays, that is to say, a progressive conservatism reduction is achieved as long as the degree of Legendre polynomials is increased. To this end, Finsler's lemma has been properly applied to decouple the Lyapunov matrices from the controller gain. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.

Decentralised tracking control based on critic learning for nonlinear disturbed interconnected systems

Abstract: In this paper, the decentralised tracking control (DTC) problem is investigated for a class of continuous-time large-scale systems with external disturbance by utilising adaptive dynamic programming (ADP). Firstly, the DTC problem is solved by designing corresponding optimal controllers of the isolated subsystems, which are formulated with N augmented subsystems consisting of the tracking error and the reference trajectory. Then, considering the external disturbance, we can effectively construct the DTC scheme by means of adding suitable feedback gains to the optimal control strategies associated with each augmented tracking isolated subsystems (ATISs). Due to the approximate nature, a series of critic neural networks are constructed to solve the Hamilton–Jacobi–Isaacs equation, so as to derive the estimation of the Nash equilibrium solution containing the optimal control strategy and the worst disturbance law. Herein, a modified weight updating criterion is developed by employing a stabilising term. Consequently, we remove the requirement of initial admissible control in the proposed algorithm. After that, stability analysis of the ATIS is performed through the Lyapunov theory, in the sense that tracking states and weight approximation errors are uniformly ultimately bounded. Finally, an experimental simulation is demonstrated to ensure the validity of the proposed DTC scheme.

Event-triggered polynomial input-to-state stability in mean square for pantograph stochastic systems

Abstract: This article investigates the polynomial integral input-to-state stability in mean square (ms-PIISS) and polynomial -weighted integral input-to-state stability in mean square ( -weighted ms-PIISS) for pantograph stochastic systems. The above stability is achieved through dynamic event-triggered mechanism and static event-triggered mechanism. To avert Zeno behaviour in each sample path, our event-triggered mechanisms (ETMs) force a pause time after each successful execution, which will lead to intermittent detection of system status, thus greatly saving communication resources. One utilises the Hanalay-type inequality to obtain the less conservative stability criterion. In addition, a collaborative design method of ETM and linear controller is proposed. Ultimately, an paraphrastic example is shown to indicate the availability of the mentioned collaborative design process.

A multi-period emergency medical service location problem based on Wasserstein-metric approach using generalised benders decomposition method

Abstract: ABSTRACT This paper considers a multi-period location and sizing problem for an emergency medical service (EMS) system based on a distributionally robust optimisation (DRO) chance-constrained programming approach. The dynamic uncertain emergency medical requests are described in the ambiguity set, which is constructed based on Wasserstein-metric. The model of this problem focuses on minimising long-term operation costs. The chance constraints ensure the reliability of EMS system for the entire geographic areas. A reformulation of chance constraints is provided in Mixed Integer Linear Program form. For problem solution, a generalised Benders decomposition (GBD) implementation is proposed. A numerical simulation is conducted to illustrate the performance of two solution approaches in terms of computational convergence speed and optimality of the problem.

Decentralised fully probabilistic design for stochastic networks with multiplicative noise

Abstract: ABSTRACT In this paper, we present an innovative decentralised control framework, designed to address stochastic dynamic complex systems that are influenced by multiple multiplicative noise factors. Our advanced approach builds upon the foundation of conventional Decentralised Fully Probabilistic Design (DFPD) by refining the Riccati equation to accommodate multiple noise sources effectively. By embracing the inherent stochastic nature of complex systems, our methodology fully characterises their dynamic behaviours using probabilistic state–space models, delivering a comprehensive representation of subsystem components. Importantly, the DFPD approach also incorporates system and input constraints by characterising their corresponding ideal distributions, ensuring optimal functionality and performance while adhering to permissible boundaries. To further enhance system performance, we introduce a probabilistic message passing architecture that enables seamless communication between neighbouring subsystems and promotes harmonised decision-making among local nodes. To demonstrate the efficacy of our proposed framework, we employ a three-inverted pendulum system as a numerical example and compare its performance to that of the conventional DFPD. Through this comparison, we showcase the advantages of our novel decentralised control approach in handling complex systems with multiple noise factors.

Online bandit convex optimisation with stochastic constraints via two-point feedback

Abstract: ABSTRACT In this paper, an online convex optimisation problem with stochastic constraints in the bandit setup is investigated. We are particularly interested in the scenario where the gradient information of both loss and constraint functions is unavailable. Under this scenario, only the values of loss and constraint functions at a few random points near the decision are provided to the decision maker after the decision is submitted. We first propose an online bandit algorithm based on the virtual queue in which two-point feedback is used to approximate the gradient feedback. Then we adopt the static benchmark to analyse the optimisation performance and establish the sub-linear expected static regret and sub-linear expected constraint violations of the proposed algorithm in the two-point bandit feedback setup. Moreover, the expected static regret and constraint violations are further improved to when loss functions satisfy the condition of strong convexity. Finally, an online job scheduling numerical simulation is shown to demonstrate the performance of the proposed method and to corroborate the theoretical guarantees.

Linear quadratic optimal control for time-delay stochastic system with partial information

Abstract: ABSTRACT This paper is concerned with the linear quadratic optimal control problem for the time-delay stochastic system with partial information. The main contribution is to derive the equivalent solvability condition and give the explicitly optimal controller under partial information in terms of the Riccati equations. The key is to explicitly solve the forward and backward stochastic difference equations with partial information, which is derived from the stochastic maximum principle.