Dynamic Control Problem Research Articles

APSO-MPC and NTSMC Cascade Control of Fully-Actuated Autonomous Underwater Vehicle Trajectory Tracking Based on RBF-NN Compensator

In this paper, a model predictive control (MPC) method optimized by an adaptive particle swarm optimization (APSO) algorithm is proposed. Combined with non-singular terminal sliding mode control (NTSMC), the inner and outer double-closed-loop control system is constructed to solve the fully actuated autonomous underwater vehicle (AUV) dynamic trajectory tracking control problem. First, the outer loop controller generates the expected optimal velocity commands and passes them to the inner loop velocity controller, which generates the available control inputs to ensure the entire closed-loop trajectory tracking. In the controller design stage, system input and state constraints are effectively considered. After that, a compensator based on an adaptive radial basis function (RBF) neural network (NN) is designed to compensate for the model error and external sea state disturbances and to improve the control accuracy of the system. Then, the stability of the proposed controller is proved based on Lyapunov analysis. Finally, the dynamic trajectory tracking performance of an AUV with different sea state disturbances is verified by simulation, and the simulation results are compared with double-closed-loop PD control and cascade control of standard MPC based on PSO and SMC. The results show that the designed controller is effective and robust.

Ultra-Reliable Distributed Cloud Network Control With End-to-End Latency Constraints

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay-constrained least-cost dynamic network control problem</i> . To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable cloud network control</i> (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

Fractional dynamic analysis and optimal control problem for an SEIQR model on complex networks.

A fractional order susceptible-exposed-infected-quarantined-recovered model is established on the complex networks. We calculate a specific expression for the basic reproduction number R0, prove the existence and uniqueness with respect to the solution, and prove the Ulam-Hyers stability of the model. Using the Latin hypercube sampling-partial rank correlation coefficient method, the influence of parameters on the R0 is analyzed. Based on the results of the analysis, the optimal control of the model is investigated as the control variables with vaccination rate and quarantine rate applying Pontryagin's minimum principle. The effects of α, degree of nodes, and network size on the model dynamics are simulated separately by the prediction correction method.

Backpressure-Based Distributed Dynamic Route Control for Connected and Automated Vehicles

This study focuses on the dynamic route control (DRC) problem for connected and automated vehicles (CAVs). The problem is usually solved using two popular methods, i.e., dynamic shortest path (DSP) and dynamic system optimal assignment (DSO). However, the DSP algorithm is unproductive under congested conditions, while the DSO, although can achieve system optimal, possesses considerable algorithm complexity. Furthermore, the two methods are usually solved in a centralized system due to the need for global information of the entire network, which is both communicationally and computationally ineffective. With the emergence of mobile edge computing (MEC), a new distributed DRC method only relying on local information is more achievable and practical. In this context, this research developed a novel DRC algorithm based on the distributed Backpressure (BP) principle for the MEC-enabled CAVs. The BP herein is modified as a function of the real-time density, which avoids the unrealistic point queue assumption in the original BP algorithm. In addition, the real-time traffic state is pre-identified to determine whether re-routing is essential, thereby reducing the possibility of CAVs guided to unnecessarily long routes. Results from the case study simulated by the traffic simulation software Simulation of Urban MObility (SUMO) indicate that the proposed method outperforms the BP method without congestion identification in low-demand cases, and the performance is significantly better than the DSP while approximating the DSO in congested cases. More importantly, both communicational cost and algorithm execution time was greatly reduced when compared with the DSO.

Finite-Time Dynamic Event-Triggered Fuzzy Output Fault-Tolerant Control for Interval Type-2 Fuzzy Systems

The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered mechanism is formulated via integrating sensor-to-observer with observer-to-controller. Third, an observer-based finite-time event-triggered fuzzy fault-tolerant controller is synthesized via the nonparallel distribution compensation design principle. Consequently, the finite-time stable conditions of the addressed IT2 fuzzy system are established by constructing an appropriate Lyapunov function. Furthermore, an output feedback control design algorithm of solving control and observer gains is given in terms of the established sufficient finite-time stable conditions. Finally, a practical example of a nonlinear tunnel diode circuit system is provided to verify the effectiveness of the proposed IT2 fuzzy control scheme.

Risk-Averse Optimal Control in Continuous Time by Nesting Risk Measures

This paper extends dynamic control problems from a risk-neutral to a risk-averse setting. We establish a limit for consecutive risk-averse decision making by consistently and adequately nesting coherent risk measures. This approach provides a new perspective on multistage optimal control problems in continuous time. For the limiting case, we elaborate a new dynamic programming principle, which is risk averse, and give risk-averse Hamilton–Jacobi–Bellman equations by generalizing the infinitesimal generator. In doing so, we provide a constructive explanation of the driver g in g-expectation, a dynamic risk measure based on backward stochastic differential equations. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Project-ID 416228727 – SFB 1410].

Adaptive Fuzzy Decentralized Dynamic Surface Control for Switched Large-Scale Nonlinear Systems With Full-State Constraints.

In this study, an adaptive fuzzy decentralized dynamic surface control (DSC) problem is investigated for switched large-scale nonlinear systems with deferred asymmetric and time-varying full-state constraints. Due to the existence of additional general nonlinearities, complicated output interconnections, and full-state constraints, it is difficult to address the above control problem using existing methods. Fuzzy-logic systems are, therefore, utilized to approximate the unknown nonlinear functions, and the DSC technique is adopted to overcome the "curse of dimensionality" problem. A novel fuzzy adaptive decentralized controller design is presented using the proposed convex combination technique. Furthermore, it is proven that under the proposed controller and state-dependent switching law, all states of the closed-loop system are bounded and deferred asymmetric, and the time-varying full-state constraints are strictly obeyed. The simulation results are presented to demonstrate the effectiveness of the proposed method.

Синтез управління стабілізацією геостаціонарного супутника у точці стояння

Розроблено методи та алгоритми балістичного забезпечення управління стабілізацією довготи точки стояння геостаціонарного супутника (ГСС). Еволюція поточних координат ГСС моделювалася диференціальними рівняннями руху матеріальної точки у центральному гравітаційному полі з урахуванням обмежених структурно-параметричних збурень відомої інтенсивності, викликаних загальним впливом Сонця, Місяця, відмінністю гравітаційного поля Землі від центрального тощо. Конкретне математичне моделювання цих збурень не розглядалося, використовувалися лише властивості їх загальної обмеженості за нормою. Для синтезу управління застосовувався відомий в теорії керування орієнтацією (кутовим положенням космічного апарата) аналог — метод декомпозиції загальної задачі синтезу на кінематичну і динамічну. Для їх вирішення використовувалися відомі узагальнення прямого методу Ляпунова дослідження стійкості розвʼязків диференціальних рівнянь на дослідження стійкості замкнених обмежених множин (конкретно багатовимірних еліпсоїдів) у їх фазових просторах. Поряд із методом синтезу управління стабілізацією довготи точки стояння ГСС запропоновано методи еліпсоїдального оцінювання, викликані впливом згаданих збурень помилок стабілізації, що встановилися. Методи засновані на побудові зовнішніх еліпсоїдальних оцінок множин досяжнос­ті та граничних множин у фазових просторах динамічних систем з обмеженими структурно-параметричними збуреннями. Запропоновано також метод отримання гарантованих інтервальних оцінок помилок стабілізації, що встановилися, по кожній з координат. Ефективність запропонованих методів керування ілюструється результатами компʼютер­ного моделювання.&#x0D;

Dynamic Hybrid-Triggered-Based Fuzzy Control for Nonlinear Networks Under Multiple Cyberattacks

This article addresses the problem of dynamic hybrid-triggered control for nonlinear networked control systems. A novel resilient control method is presented to improve network resource utilization and system performance against cyberattacks for the underlying systems. First, a fuzzy-model-based system is established to describe the nonlinear networked systems subject to multiple cyberattacks, external disturbances, parameter uncertainties, and network-induced delays. Then, a resilient dynamic hybrid-triggered scheme is proposed to conserve the network bandwidth and reduce the effect of cyberattacks. A new codesign method of the observer-based fuzzy controller and the resilient dynamic hybrid-triggered scheme is developed. Sufficient conditions in terms of linear matrix inequalities are given such that the closed-loop system is global exponential stable with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> disturbances attenuation level. Finally, an example of lateral control in autonomous driving is given to verify the effectiveness of the developed control method.

Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach

This article studies a dynamic event-triggered security control problem for networked control systems subject to deception attacks and packet dropouts. First, a combined cyber-attack model is proposed, which utilises two sets of independent stochastic sequences to reflect randomly occurring cyber-attacks. Subsequently, a dynamic event-triggered protocol is constructed to relieve the restricted bandwidth pressure by reducing the data transmission of the communication channel from the plant to the controller. With the consideration of randomly occurring deception attacks, packet dropouts, and dynamic event-triggered protocols, an online model predictive control algorithm is established to ensure the stochastic stability of the closed-loop model with expected H2/H∞ performance. Finally, two examples are simulated to interpret the validity and effectiveness of the proposed design strategy.

Dynamic learning from neural network‐based control for sampled‐data strict‐feedback nonlinear systems

AbstractThis article focuses on the dynamic learning and control problem for a class of nonlinear sampled‐data systems in strict‐feedback form. To achieve learning of unknown system dynamics in closed‐loop control, two obstacles need to be overcome: the inherent noncausal problem in the backstepping design of discrete‐time nonlinear systems, and the exponential stability of the sampling closed‐loop error system. First, a novel command filtered adaptive neural control is developed to achieve closed‐loop stability and the convergence of output tracking error based on Lyapunov theory, the noncausal problem is overcome by utilizing the command filter technique, and the ‐step delay is avoided in the recursive process. Second, by combining a state transformation method, the closed‐loop error system is divided into perturbed linear time‐varying (LTV) subsystems. Based on the convergence of the tracking errors, the partial persistent excitation (PE) condition of the radial basis function network is verified and established recursively. By extending the exponential stability result of a class of discrete‐time LTV systems, the convergence of the estimated weights to their optimal values is guaranteed with the PE condition. Subsequently, locally accurate approximation/learning of the unknown closed‐loop sampled dynamics can be obtained and stored in the form of constant neural networks. Finally, by reutilizing the acquired knowledge, a learning‐based control scheme for the sampled‐data system is further proposed to pursue improved control performance. Simulation studies are performed to illustrate the validity of the proposed scheme.

Compensation of external disturbances for MIMO systems with control delay

The problem of compensation of external disturbing influences for MIMO system with input delay is important and relevant. A solution to this problem is proposed in the problems of dynamic objects control and in a number of others. The proposed method is based on the principle of an internal model and requires the identification of perturbation parameters. At the first stage, a scheme for extracting a disturbance is presented which is represented as a sinusoidal signal with an unknown frequency, amplitude, and phase. At the second stage, the problem of identifying the frequencies of a sinusoidal and multisinusoidal signal is solved. In the last stage, an algorithm for stabilizing the state of the object to zero is developed using feedback. A new scheme for compensating external disturbances for a MIMO system with an input delay is proposed. A new algorithm for identifying the frequencies of a multisinusoidal signal is proposed. The analysis of the possibilities of the proposed estimation method using computer simulation in the MATLAB Simulink environment is carried out. The developed method can be effectively applied to a wide class of applied tasks related to the control of robots and robotic manipulators for various purposes.

A real-time and eco-layout platform for optimization of supply/costs for water distribution systems management

Abstract This research discusses developing a coupled system with both structural and operational aspects of the water distribution system. Operating view is provided through a real-time control scheduler (RTC), which satisfactorily attends to solve the dynamic control problem at every timestep via minimization of energy costs over the day and satisfying hydraulic reliability constraints through suggesting near-optimal pump schedules. Moreover, the network layout provides the structural view with a focus on inventing the most prosperous cost-effective topology. The modeling process was exerted by group-based machine learning techniques, a dynamic random search (DDS) algorithm and ultimately, the model performance was evaluated. In addition to providing stable hydraulic conditions, evaluating the coupled optimization platform (RTC-eco-layout) formulation on a typical day was adequate to beget an average of up to 23% daily energy savings as well as preserving the hydraulic feasibility properties in the range of up to 30% energy cost economies. The sensitivity analysis admitted that both operating and environmental variables contribute to the total uncertainty.

Distributed dynamic rendezvous control of the AUV-USV joint system with practical disturbance compensations using model predictive control

This paper addresses the distributed dynamic rendezvous control problem of the AUV-USV heterogeneous joint system in the presence of unknown external disturbances using nonlinear model predictive control. By utilizing the nonlinear model predictive control framework, the rendezvous problem can be solved without constructing explicit error dynamics of the heterogeneous system. The inputs and states constraints such as spatial and velocity constraints can also be considered simultaneously in an intuitive way. To facilitate distributed implementations, distributed optimization problems with incorporated moving suppression are then constructed to guarantee nominal stability. Considering unknown external disturbances, a practical disturbance compensating method is finally proposed, ensuring the satisfaction of state constraints. The nominal recursive feasibility and stability are proved in this paper. Compared with existing works, the proposed scheme improves the applicability and robustness against external disturbances for dynamic rendezvous between two heterogeneous robots. Simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed method.

Model-Based Dynamic Event-Triggered Control for Cyber-Physical Systems Subject to Dynamic Quantization and DoS Attacks

In this article, the problem of model-based dynamic event-triggered control (ETC) of cyber-physical systems (CPSs) with dynamic quantization effects and denial-of-service (DoS) attacks is studied. With the aid of a technique combining model-based CPSs and dynamic ETC scheme, the number of transmissions is significantly reduced to save the scarce network resources. An average dwell-time (ADT) automaton and a time-ratio monitor are firstly introduced to ETC systems to describe the frequency and duration of the DoS attacks, then a unified hybrid framework of CPSs with dynamic quantizer is established. In the presence of external disturbances, quantization effects and DoS attacks, by following the emulation approach some event-triggering conditions are designed to allow the robustness, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> stability and the existence of a minimum inter-event time (MIET). Relying on a novel Lyapunov function, we derive new sufficient conditions on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -gain performance of CPSs. Moreover, the Zeno phenomenon in the quantized ETC system is excluded due to the existence of a positive MIET. Finally, a numerical example is provided to demonstrate the feasibility of the approach proposed in this paper.

Prescribed performance dynamic surface control for nonlinear systems subject to partial and full state constraints

This paper discusses prescribed performance dynamic surface control (DSC) problems for nonlinear systems subject to state constraints. Incorporating the prescribed performance control (PPC) with DSC methods, we present a neural adaptive control method that characterizes the steady-state tracking error and its convergence rate. By constructing a type of funnel error variables, the error variables can be limited in the boundary of funnel functions, and the violations of the conditions of partial and full state constraints are shunned. In the latter case, a new assumption is proposed to handle the circular design problem in the design procedure. Under the control scheme designed, the considered nonlinear systems can be stabilized with the Lyapunov stability theory, and it can be guaranteed that all variables are semi-globally uniformly ultimately bounded (SGUUB). Finally, simulation results verify that the proposed prescribed performance DSC-based control scheme can ensure stability and prescribed performance metrics in the existence of partial and full-state constraints.

Observer-based asynchronous event-triggered control for interval type-2 fuzzy systems with cyber-attacks

This study investigates the observer-based asynchronous dynamic event-triggered control problem for networked interval type-2 fuzzy systems (IT-2 FSs) suffering from hybrid cyber-attacks, whereby the deception attacks (DAs) and the non-periodic denial-of-service (DoS) attacks are simultaneously considered. Contrasting with existing works, a new resilient dynamic event-triggering mechanism (DETM) is employed in the design process of the controller in the event of hybrid cyber-attacks. It is concluded that the resilient condition of DETM is adaptively varied based on current sampling, latest available packets, and a discrete dynamic function. Using a piecewise Lyapunov functional (PLF), new stochastically exponential stability conditions are developed to ascertain the link among asynchronous DETM, DAs, non-periodic DoS attacks and mismatched membership functions (MMFs). These results can further reduce the data transmissions and guarantee the exclusion of the Zeno behavior. The effectiveness of the concerned system under hybrid cyber-attacks is ultimately verified by a numerical simulation.

Dynamic event-triggered control for nonlinear NCSs subject to DoS attacks

In this study, a dynamic event-triggered control problem is addressed for nonlinear networked control systems (NCSs) subject to denial-of-service (DoS) attacks. Assume that data from the plant to the controller is transmitted via a wireless transmission channel under malicious DoS attacks characterized by frequency and duration properties. On the premise of ensuring the stability and minimum inter-event time (MIET) of the systems, dynamic event-triggered mechanisms (DETMs) are proposed for the hybrid dynamic system to withstand a certain degree of DoS attacks. Three event-triggered schemes are designed for the most existing state-based control systems which further enlarge the inter-event times, and the stabilization conditions of hybrid dynamic system are given. Finally, illustrative examples are provided to verify the effectiveness of the presented theoretical results.

Optimisation of Airline Dynamic Multileg Capacity Control Problem considering Competition from High-Speed Rail

This paper develops an optimisation model to address the dynamic multileg capacity control problem for the airline, taking into account the competition from the high-speed rail (HSR). Conventional capacity control models assume that the passenger demand is independent and that airlines control capacity independently. The proposed model considers the semi-independent demand, i.e., a customer of certain fare class arrives and makes choices from available alternatives provided by both airline and HSR. If the customer’s first option is rejected, their booking request will be diverted both horizontally to the other operator and vertically to parallel flights within the same operator. Making use of this assumption, the customer’s second and third choices are captured. With this model, airlines can control the sales process to determine whether or not to accept an initial booking request or diverted booking request to maximise their revenue. The deterministic linear programming (DLP) method is proposed as a model solution, enabling the computation of partitioned allocation and the bid price of each product. The optimal capacity control strategy is then obtained based on bid price control. To validate the model, numerical experiments are conducted to simulate customers' actual arrival and to show that the model is capable of producing better revenue, especially in the event that demand exceeds supply. Moreover, the demand diversion effect brings more revenue for both airline and HSR, and this potential revenue source has been ignored by previous research studies.

Turnpike Property in Optimal Microbial Metabolite Production

We consider the problem of maximization of metabolite production in bacterial cells formulated as a dynamical optimal control problem (DOCP). According to Pontryagin’s maximum principle, optimal solutions are concatenations of singular and bang arcs and exhibit the chattering or Fuller phenomenon, which is problematic for applications. To avoid chattering, we introduce a reduced model which is still biologically relevant and retains the important structural features of the original problem. Using a combination of analytical and numerical methods, we show that the singular arc is dominant in the studied DOCPs and exhibits the turnpike property. This property is further used in order to design simple and realistic suboptimal control strategies.