Presented Control Scheme Research Articles

Neural-Network-Based Adaptive Fixed-Time Control for Nonlinear Multiagent Non-Affine Systems.

In this research, the adaptive neural network consensus control problem is addressed for a class of non-affine multiagent systems (MASs) with actuator faults and stochastic disturbances. To overcome difficulties associated with actuator faults and uncertain functions of the designed MAS, a neural network fault-tolerant control scheme is developed. Moreover, an adaptive backstepping controller is developed to solve the non-affine appearance in multiagent stochastic non-affine systems using the mean value theorem. Being different from the existing control methods, the developed adaptive fixed-time control approach can ensure that the outputs of all followers track the reference signal synchronously in the fixed time, and all signals of the controlled system are semi-globally uniformly fixed-time stable. The simulation results confirm that the presented control strategy is effective in achieving control goals.

Event-Triggered Robust Adaptive Dynamic Programming for Multiplayer Stackelberg-Nash Games of Uncertain Nonlinear Systems.

In this article, an event-triggered robust adaptive dynamic programming (ETRADP) algorithm is developed to solve a class of multiplayer Stackelberg-Nash games (MSNGs) for uncertain nonlinear continuous-time systems. Considering the different roles of players in the MSNG, the hierarchical decision-making process is described as the designed value functions for the leader and all followers, which assist to transform the robust control problem of the uncertain nonlinear system into an optimal regulation problem of the nominal system. Then, an online policy iteration algorithm is formulated to solve the derived coupled Hamilton-Jacobi equation. Meanwhile, an event-triggered mechanism is designed to alleviate computational and communication burdens. Moreover, critic neural networks (NNs) are constructed to obtain the event-triggered approximate optimal control polices for all players, which constitute the Stackelberg-Nash equilibrium of the MSNG. By using Lyapunov's direct method, the stability of the closed-loop uncertain nonlinear system is guaranteed under the ETRADP-based control scheme in the sense of uniform ultimate boundedness. Finally, a numerical simulation is provided to demonstrate the effectiveness of the present ETRADP-based control scheme.

A Concurrent Event-Triggered Approach for Fuzzy Adaptive Control of Nonlinear Strict-Feedback Systems.

If a control input signal constructed by backstepping is used to generate an event-triggered control signal, then not only the real control input is discontinuous, but all the virtual control signals that make up the control signal are also discontinuous and have the same triggering instants with the control input. This fact makes the control design and stability analysis more difficult and complex via backstepping. This article focuses on this problem and works at coming up a new backstepping design procedure of event-triggered fuzzy adaptive control for nonlinear strict feedback systems. A concurrent event-triggered mechanism is proposed to ensure that the real control input and the virtual control signals have the same triggering instants. Then, a systemic backstepping design procedure is developed to construct event-triggered fuzzy adaptive controller. The Lyapunov technique for nonlinear impulsive system is employed to provide the closed-loop stability analysis. It is shown that the derived event-triggered fuzzy adaptive controller ensures that the closed-loop system output well follows the desired tracking trajectory and the other closed-loop signals remain bounded. Eventually, two examples are provided to further verify the applicability and reliability of the presented control scheme.

Predictive Temperature Control of an Industrial Heating Process

This paper presents an approach to control the temperature of an industrial heating process where a workpiece is transported through a heating unit in order to reach a certain temperature. To control this process is challenging due to the variable and possibly abruptly changing feed rate while requiring the final workpiece temperature to be maintained at a constant value. The speed itself cannot be influenced by the control system. However, its future trajectory is known to a certain extent at runtime. A model predictive controller is presented, which is characterized by the fact that the prediction horizon is dynamically adjusted to the transportation speed, that the traveled distance of the workpiece within the horizon is constant. Moreover, the step size of the integrator is also adapted, yielding a constant number of integration steps despite large variations in velocity. Because of the special structure of the used model, the system state cannot be reused from one time step to the next and needs to be reconstructed in a receeding horizon fashion, even if the model was perfect. This is also a distinctive feature of the presented control scheme. Simulation and measurement results are provided, showing that the presented controller achieves promising results and outperforms the existing lookup-table-based controller currently in use.

Asynchronous Control for Interval Type-2 Fuzzy Nonhomogeneous Markov Jump Systems Against Successive DoS Attacks.

This article is concerned with the problem of asynchronous control for Interval Type-2 (IT2) fuzzy nonhomogeneous Markov jump systems against successive denial-of-service (DoS) attacks. The system and the controller are assumed to be connected through a communication channel subject to malicious attacks. The maximum number and probability distribution of successive attacks are considered. Under the imperfect premise matching, a fuzzy asynchronous controller is constructed by the hidden Markov model. By means of the introduced transmission delay, a delay closed-loop system is constructed, where the stochastic description of the delay depends on the statistical characteristic of successive attacks. Then stability criteria together are derived in the form of linear matrix inequalities by the Lyapunov functional approach, as well as the condition on the existence of the fuzzy controller. Finally, the feasibility and effectiveness of the presented control scheme are demonstrated by simulation results.

Neural-Network-Based Adaptive Event-Triggered Asymptotically Consensus Tracking Control for Nonlinear Nonstrict-Feedback MASs: An Improved Dynamic Surface Approach.

In this article, the asymptotic tracking control problem for a class of nonlinear multi-agent systems (MASs) is researched by the combination of radial basis function neural networks (RBF NNs) and an improved dynamic surface control (DSC) technology. It's important to emphasize that the MASs studied in this article are nonlinear and nonstrict-feedback systems, where the nonlinear functions are unknown. In order to satisfy the requirement that all items in the controller must be available, the unknown nonlinearities in the system are flexibly approximated by utilizing RBF NNs technique. Moreover, the issue of ``complexity explosion'' in the backstepping procedure is handled by improving the traditional DSC technology, and meanwhile, the influences of the boundary layers caused by the filters in the DSC procedure are eliminated skillfully through the compensation terms. In addition, the relative threshold event-triggered strategy is developed for the designed controllers to reduce the waste of communication resources, where Zeno phenomenon is successfully avoided. It is observed that the new presented control strategy ensures that all the closed-loop systems variables are uniformly ultimately bounded (UUB), and furthermore all the outputs of followers are able to track the output of the leader with zero tracking errors. Finally, the simulation results are presented to show the effectiveness of the obtained design scheme.

Event-triggered fuzzy controller co-design for networked systems with denial-of-service attacks and its applications

This manuscript focuses on a controller co-design problem of networked interval type-2 (IT2) fuzzy systems with denial-of-service (DoS) attacks via event-triggered mechanism. A Lyapunov-Krasovskii functional with the sampling property is established to execute stability analysis. New sufficient conditions are acquired for the existence of event-triggered fuzzy controller, and relaxed results are realized. The switched closed-loop system is guaranteed to be exponential stable, and an H∞ performance is reached. At last, the presented control schemes are used in two engineering systems, which demonstrate the efficiency of the developed control strategy.

Present status, future trends, and control strategies of invasive alien plants in China affected by human activities and climate change

Invasive alien plants (IAPs) have serious environmental and economic impacts, especially in vulnerable areas of China. However, IAP richness distribution patterns, their driving factors, and the dynamic shifts in potential distribution areas remain elusive. We assessed IAP richness distribution patterns and drivers using 402 IAPs recorded in China at 88 926 occurrence points, and then predicted their potential distribution areas. The results show that IAP hotspots were mainly located in southeastern China, especially coastal areas of the South and East and large inland cities. Population density, gross domestic product (GDP), and four climate variables associated with precipitation and temperature jointly influenced the richness distribution pattern of all IAPs. Specifically, population density and GDP impacted the richness distribution pattern of narrow‐range IAPs, and population density, GDP, distance to the nearest national highway, and five climate variables affected the richness distribution pattern of widespread IAPs. Only GDP contributed significantly to the richness distribution pattern of the top 5% hotspot grid cells, whereas population density, GDP, and precipitation in the driest month (BIO14) significantly influenced the richness distribution patterns of hotspots for both the top 10% and top 20%. Prediction analysis demonstrated that southeastern China would have particularly high invasion risk under both current and future climate scenarios. Regions with increases in predicted species richness are more common (44.83–64.97%) than those with decreases, except under the Representative Concentration Pathway (RCP) 4.5 scenario. Climate change will contribute greatly to the expansion of potential IAP distribution areas under both optimistic (RCP 2.5) and pessimistic scenarios (RCP 8.5). The results of this study provide insights into the priority management of IAPs by developing promising strategies for the control and prevention of IAP invasion.

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Article Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate Manman Yuan 1,2,*, and Wei Qian 1,2,* 1 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China 2 Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China * Correspondence: ymmamhappy@163.com; qwei@hpu.edu.cn Received: 4 September 2023 Accepted: 7 November 2023 Abstract: In this paper, the problem of adaptive output feedback tracking is considered for a class of nonlinear systems with lower-triangular structures. A novel dynamic gain is introduced to deal with the unknown growth rate. By coupling the dynamic gain with the observer and the controller, an adaptive output tracking controller is developed, which can guarantee that all signals of the closed-loop system are globally bounded. Finally, the effectiveness of the presented control scheme is illustrated by a numerical example.

A new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning

In this paper, a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper. The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods. To achieve optimal control, RL algorithm based on critic–actor architecture is considered for the nonlinear system. Due to the significant security risks of network transmission, the system is vulnerable to deception attacks, which can make all the system state unavailable. By using the attacked states to design coordinate transformation, the harm brought by unknown deception attacks has been overcome. The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded. Finally, the simulation experiment is shown to prove the effectiveness of the strategy.

Adaptive Fuzzy Fault-Tolerant Control of Uncertain Fractional-Order Nonlinear Systems with Sensor and Actuator Faults

In this work, an adaptive fuzzy backstepping fault-tolerant control (FTC) issue is tackled for uncertain fractional-order (FO) nonlinear systems with sensor and actuator faults. A fuzzy logic system is exploited to manage unknown nonlinearity. In addition, a novel FO nonlinear filter-based dynamic surface control (DSC) method is constructed, effectively avoiding the inherent complexity explosion problem in the backstepping recursive process, and in the light of the construction of auxiliary functions, compensating the coupling term introduced by faults. On account of certain assumptions, the stability criterion of the FO Lyapunov function is applied to guarantee the stability of the closed-loop system. Finally, the simulation example verifies the validity of the presented control strategy.

Adaptive Fixed-Time Control for State-Constrained High-Order Uncertain Nonlinear Cyber-Physical Systems Under Malicious Attacks

A novel fixed-time adaptive fuzzy tracking control strategy is developed to resolve the fixed-time tracking control issue for a category of state-constrained high-order uncertain nonlinear cyber-physical systems (CPSs), which suffer from malicious attacks launched in controller-actuator (C-A) channel. The proposed control strategy is independent of the exact system model and can accommodate external disturbances and malicious attacks. Meanwhile, distinguished from the existing control methods for state-constrained uncertain CPSs under malicious attacks which can only ensure the system performance when time leans towards infinity or in finite time, the presented control strategy can guarantee the system performance in fixed time. It has been substantiated that the developed control strategy can make sure that the output tracking error converges to a predefined small neighborhood of the origin, and all signals of the resulting closed-loop system satisfy the corresponding time-varying state constraints during the total operation. In particular, the settling time and tracking precision are known and can be preconfigured by selecting the design parameters appropriately. More significantly, all the initial states of the system are independent of the developed fixed-time adaptive fuzzy tracking control law and can be arbitrarily chosen in the constrained region specified by the corresponding time-varying state constraints conditions. Ultimately, a representative simulation is supplied to illustrate the effectiveness and superiority of the proposed control scheme.

Prescribed-Time Formation Control for a Class of Multiagent Systems via Fuzzy Reinforcement Learning

This paper concerns optimal prescribed-time forma- tion control for a class of nonlinear multi-agent systems (MASs). Optimal control depends on the solution of the Hamilton-Jacobi- Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the rein- forcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.

Finite‐time prescribed performance adaptive fuzzy output feedback control for stochastic nonlinear systems with dead‐zone input

AbstractIn this paper, the issue of finite‐time prescribed performance control (PPC) is addressed for stochastic nonlinear systems with the effect of dead‐zone. Fuzzy logic systems are utilized for identifying unknown nonlinear characteristics. A model consisting of linear and disturbance‐like terms is constructed to approximate the nonlinearity of dead‐zone. Meanwhile, a state observer is employed to estimate unmeasured states. To improve the convergence rate, a finite‐time performance function is introduced, which transforms the tracking error of original system to error of converted system, thus prescribing the transient and steady‐state performance of the system. Eventually, based on backstepping technique, a finite‐time PPC strategy is proposed, which ensures the boundedness of all signals in closed‐loop system. Simultaneously, the tracking error converges to a prescribed region in finite‐time. Finally, two simulation examples are provided to verify the effectiveness of presented control strategy.

Model-free optimal robust tracking control for the joint actuated by pneumatic artificial muscles with saturated air pressure input

At present, positioning and tracking the joint angle of the PAM-actuated system is still a challenging issue, due to the inherent hysteresis, high nonlinearities, and time-varying characteristics. Considering the problem of uncertain parameters, external disturbance, and saturated input, a model-free optimal robust tracking control scheme is proposed for the PAMs system in this paper. Also, a nonquadratic cost function is introduced to address the saturation air pressure input. Moreover, an integral reinforcement learning(IRL) algorithm is developed to solve the H∞ tracking Hamilton–Jacobi–Isaacs(HJI) equation. And the IRL algorithm is implemented on a critic–actor-disturbance neural network (NN) structure. The presented control scheme avoids the complete system dynamic and simultaneously compensates for the uncertain parameters, and external disturbance. Then, the effectiveness of the optimal robust tracking controller is verified via computational simulation and keeps the system stable. In addition, the tracking experiment is implemented on a hardware platform, indicating that the presented approach achieves satisfactory tracking performance.

Optimization-based power management for battery/supercapacitor hybrid energy storage system with load estimation capability in a DC microgrid

This paper proposes a novel optimization-based power management strategy (PMS) for a battery/supercapacitor hybrid energy storage system (HESS) with a semi-active structure in a DC microgrid application. As the DC bus voltage regulation is the main purpose of the HESS, the supercapacitor control due to direct connection to the DC bus becomes more important. While, in the semi-active structure, there is no control over the supercapacitor. The proposed PMS solved this challenge by considering the supercapacitor current as a control target in determining the reference current of the battery. Extending the battery life span by drawing smooth current from the battery and responding the supercapacitor to load current changes, and charging the battery with a constant current as a new objective function, are the other optimization targets. This multi-objective problem is solved by the Karush–Kuhn–Tucker (KKT) condition which makes the proposed PMS suitable for real-time applications. In addition, in contrast to previous related studies, (i) the load current (grid excess/deficiency power) as one of the important inputs of PMS is estimated by the adaptive Kalman filter (AKF), and (ii) the converter control signal is determined by system average model instead of conventional PI controller. Finally, using simulation, the performance of the proposed PMS has been verified by comparing the results with fuzzy logic, droop control, and adaptive filter-based methods. As well, the presented control strategy has been implemented on an experimental setup, and its performance is investigated.

Fault estimation and tolerant bumpless transfer control for switched systems

A novel fault estimation and tolerant bumpless transfer control strategy is proposed for switched systems. In this control scheme, a switched estimator, a switched estimator-based tolerant bumpless transfer controller and a switching logic are jointly designed. The estimator relaxes the assumption on the usual observer matching condition in the fault estimation issue of switched linear systems. The controller can tolerate the fault with small input bumps at switching time. The switching law is an improvement of the traditional state-dependent switching law with dwell time still maintained, permitting the increase of the Lyapunov function for subsystems within the ensured dwell time interval. Through the presented control scheme, a criterion is developed to drive the solvability of the fault estimation and tolerant bumpless transfer control issue for the SSs. Also, with this criterion, this control issue of subsystems can be unsolvable. An example of regulating the current for a chua’s circuit system is provided for purpose of verifying the theoretical result.

Adaptive event-triggered PID load frequency control for multi-area interconnected wind power systems under aperiodic DoS attacks

In this article, an adaptive event-triggered proportional–integral–derivative (PID) load frequency control (LFC) strategy is designed for multi-area interconnected wind power systems under aperiodic denial-of-service (DoS) attacks. First, to relieve the frequency fluctuation caused by wind power integration, a dynamic model including the doubly-fed induction generator is established for multi-area interconnected wind power systems. Second, to avoid superfluous occupation of network communication bandwidth, an adaptive event-triggered mechanism with memory property is proposed under aperiodic DoS attacks. Then, with the aid of intermittent control method, a memory-based PID LFC strategy is achieved to stabilize the frequency of wind power systems. Third, by constructing a switched Lyapunov-Krasovskii functional, two exponential stability criteria meeting the H∞ norm bound are derived. Finally, simulation examples are provided to verify the effectiveness of the presented control strategy.

Prescribed Performance Tracking Control Under Uncertain Initial Conditions: A Neuroadaptive Output Feedback Approach.

This work is concerned with the prescribed performance tracking control for a family of nonlinear nontriangular structure systems under uncertain initial conditions and partial measurable states. By combining neural network and variable separation technique, a state observer with a simple structure is constructed for output-based finite-time tracking control, wherein the issue of algebraic loop arising from a nontriangular structure is circumvented. Meanwhile, by using an error transformation, the developed control scheme is able to ensure tracking with a prescribed accuracy within a pregiven time at a preassigned convergence rate under any bounded initial condition, eliminating the long-standing initial condition dependence issue inherited with conventional prescribed performance control methods, and guaranteeing the predeterminability of convergence time simultaneously. Two simulation examples also demonstrate the effectiveness of the presented control strategy.

Dynamic analysis of a new 4D fractional-order financial system and its finite-time fractional integral sliding mode control based on RBF neural network

In this paper, considering the combined effect of interest rate and investment demand on the price index, a new 4D fractional-order financial system is established. Based on the local stability theory, the stability of equilibrium points and static bifurcation phenomena are discussed. Subsequently, to further analyze the dynamic behaviors of the new financial system, numerical tools such as bifurcation diagrams, Lyapunov exponents, phase diagrams, 0–1 test diagrams, and complexity diagrams are employed. It is found that, for a suitable selection of the differential order or system parameter, the proposed system can generate periodic oscillations with different period and chaotic attractors with different shapes. Furthermore, a new controller combining radial basis function (RBF) neural network with fractional-order integral sliding mode control is proposed to realize the finite-time synchronization for a class of fractional-order systems. Finally, the effectiveness of the presented control scheme is verified by numerical simulations and the influence of RBF neural network parameters on the control performance is analyzed.