Compound Disturbance Research Articles

Predefined-time fractional-order time-varying sliding mode control for arbitrary order systems with uncertain disturbances

This paper proposes a fractional-order time-varying sliding mode control method with predefined-time convergence for a class of arbitrary-order nonlinear control systems with compound disturbances. The method has global robustness and strongly predefined-time stability. All state errors of the system can converge to zero at a desired time, which can be set arbitrarily with a simple parameter. The strongly predefined-time convergence of the system is clearly demonstrated by the analytic expression of state error, which is obtained by solving fractional-order differential equations corresponding to the sliding mode function. The simulation results show that the proposed method still has good control performance in the presence of input saturation and external interference.

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

This paper focuses on the collision-free formation tracking of autonomous underwater vehicles (AUVs) with compound disturbances in complex ocean environments. We propose a novel finite-time extended state observer (FTESO)-based distributed dual closed-loop model predictive control scheme. Initially, a fast FTESO is designed to accurately estimate both model uncertainties and external disturbances for each subsystem. Subsequently, the outer-loop and inner-loop formation controllers are developed by integrating disturbance compensation with distributed model predictive control (DMPC) theory. With full consideration of the input and state constraints, we resolve the local information-based DMPC optimization problem to obtain the control inputs for each AUV, thereby preventing actuator saturation and collisions among AUVs. Moreover, to mitigate the increased computation caused by the control structure, the Laguerre orthogonal function is applied to alleviate the computational burden in time intervals. We also demonstrate the stability of the closed-loop system by applying the terminal state constraint. Finally, based on a connected directed topology, comparative simulations are performed under various control schemes to verify the robustness and superior performance of the proposed scheme.

Modeling compound hydrologic disturbances in the Rio Grande Headwaters

AbstractIn recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post‐disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid‐elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post‐disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions.

Distributed neural-based fault-tolerant control of multiple flexible manipulators with input saturations

This paper focuses on a novel fault-tolerant control design for networked flexible manipulators with actuator failures, input saturations, and external disturbances. Unlike the existing methods, the new control strategy has three characteristics. First, the second-order auxiliary system is used to eliminate the influence of input saturation. Second, by designing some proportional–integral (PI) auxiliary variables, the boundary dynamics of manipulators are reconstructed as some new second-order systems, where the nonlinear uncertainties are estimated by the adaptive radial basis neural networks (RBFNNs) and the compound disturbances are compensated by the adaptive method. Third, a boundary local angle interaction protocol is given under a digraph. Then combining the virtual control and Lyapunov design, some novel distributed fault-tolerant control algorithms are obtained to ensure that the vibration states and the angle consensus error are uniformly ultimately bounded. Finally, numerical simulations are provided to show the effectiveness of these control algorithms.

Robust distributed formation control with prescribed performance for nonlinear multi‐agent systems subjected to compound disturbances

SummaryThis article is concerned with the distributed leader‐following formation control for nonlinear multi‐agent systems (MASs) subject to compound disturbances. Three aspects of the control objective: formation control, collision avoidance and connectivity preservation are achieved by the designed controller composed of three control laws. The formation control law is developed based on the barrier Lyapunov function and the backstepping method, which can prescribe the performance of the close‐loop system to some extent. Within our scheme, an adaptive finite‐time disturbance observer is used to enhance the robustness of the control system against compound disturbances including parameter perturbations, actuator bias faults and external disturbances. Meanwhile, a nonlinear tracking differentiator is introduced for each follower to cancel complex differential calculations and the requirement for the leader's acceleration information. Furthermore, the auxiliary control laws for collision avoidance and connectivity preservation are designed based on the artificial potential field method, which plays an important role during the formation shaping. Finally, complete stability analysis and vivid numerical simulation are carried out.

Disturbance observer based actor-critic learning control for uncertain nonlinear systems

This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances. The proposed control algorithm integrates a filter-based design method with actor-critic learning architecture and disturbance observer to circumvent the unmodeled dynamic and the time-varying disturbance. To be specific, the actor network is employed to estimate the unknown system dynamic, the critic network is developed to evaluate the control performance, and the disturbance observer is leveraged to provide efficient estimation of the compounded disturbance which includes the time-varying disturbance and the actor-critic network approximation error. Consequently, high-gain feedback is avoided and the improved tracking performance can be expected. Moreover, a composite weight adaptation law for actor network is constructed by utilizing two types of signals, the cost function and the modeling error. Eventually, theoretical analysis demonstrates that the developed controller can guarantee bounded stability. Extensive simulations and experiments on a robot manipulator are implemented to validate the performance of the resulted control strategy.

Wildfires correlate with reductions in aboveground tree carbon stocks and sequestration capacity on forest land in the Western United States

In the United States (US), forest ecosystems are the largest terrestrial carbon sink, offsetting the equivalent of >12 % of economy-wide greenhouse gas (GHG) emissions annually. In the Western US, wildfires have shaped much of the landscape by changing forest structure and composition, increasing tree mortality, impacting forest regeneration, and influencing forest carbon storage and sequestration capacity. Here, we used remeasurements of >25,000 plots from the US Department of Agriculture, Forest Service Forest Inventory and Analysis (FIA) program and auxiliary information (e.g., Monitoring Trends in Burn Severity) to characterize the role of fire along with other natural and anthropogenic drivers on estimates of carbon stocks, stock changes, and sequestration capacity on forest land in the Western US. Several biotic (e.g., tree size, species, and forest structure) and abiotic factors (e.g., warm climate, severe drought, compound disturbances, and anthropogenic interventions) influenced post-fire tree mortality and regeneration and had concomitant impacts on carbon stocks and sequestration capacity. Forest ecosystems in a high severity and low frequency wildfire regime had greater reductions in aboveground biomass carbon stocks and sequestration capacity compared to forests in a low severity and high frequency fire regime. Results from this study can improve our understanding of the role of wildfire along with other biotic and abiotic drivers on carbon dynamics in forest ecosystems in the Western US.

Quantification of coastal transportation network resilience to compounding threats from flooding and anthropogenic disturbances: A New York City case study

Technological advancements and management adaptations have improved the function of engineered systems in response to threats from coastal flooding. Other natural and anthropogenic disturbances such as pandemics, utility hijacking, infrastructure destruction, and biochemical releases can stress a system beyond acceptable limits or in ways not previously conceived. Such threats can be direct or indirect and often result in large-scale disruption to the critical functions of the system. Traditional risk management approaches, while effective for known and predictable threats, are not adequate preparation for compound disturbances that are often unpredictable and not well defined. Rather, a resilience-based approach is required. Resilience-based approaches acknowledge that multiple disruptions to the system will occur, such as the interaction of impending hurricanes and evacuation complications due to a pandemic, while focusing on the recovery and maintenance of critical functions. The system can be stressed with multiple disturbances to determine its capacity to resist and recover. Analysis of these capacities or subsequent failures can then be used to determine the resilience of the system and provide insight into remedial actions or improvements. A framework combining hydrologic modeling and network science can be used to determine critical weaknesses in transportation infrastructure due to compounding threats. This determination could be used to address pre-disaster staging by identifying areas that are likely to be isolated and to identify the characteristics of a resilient network to incorporate into future designs. To analyze resilience under compounded disturbances, coastal flood modeling is combined with hypothetical vehicle bridge failure in New York City, USA, and connectivity is analyzed through the use of ego networks. Our results show network analysis can be effectively used to identify areas of need to improve whole network resilience and is a valuable tool to quantify the compounding effects of multiple threats.

Post-fire and harvest legacy on soil carbon and microbial communities in boreal forest soils

The boreal forest is currently facing multiple disturbances, which are increasing in extent, frequency and severity. Of particular concern in the western boreal forest of Canada are wildfires and harvesting, which, in the short-term, may impact the forest floor, a storehouse of organic matter and site of highest soil microbial activity. However, how these disturbances may individually and cumulatively affect soil properties during ecosystem recovery is not well documented. Here we compared the separate and compound (salvage-logging) effects of wildfire and harvesting in the mixedwood boreal forest of northern Alberta. Ten years following disturbance, carbon chemical composition of the harvested forest floors was comparable to the control plots as was indicated by solid-state nuclear magnetic resonance analysis. We report a legacy effect of fire in recovering forest floors, with the continued presence of black carbon. Further, as determined by phospholipid fatty acid analysis (PLFA), both wildfire and salvage-logging disturbance resulted in distinct microbial composition from the control and harvest treatments. A shift in organic matter composition indicative of fresh plant inputs was determined to be a key driver of community differences. Despite the presence of black carbon, and shallower depth, the fire-disturbed forest floor harbored the greatest microbial biomass. However, this greater microbial biomass was not present following salvage-logging of the fire disturbed stands. Instead, the salvage-logged stands had the shallowest forest floor, low microbial biomass, and differed the most from the control forest floors based on PLFA results. Taken together, these results indicate that, ten years after disturbance, the compound disturbance of salvage-logging had a greater impact on the recovery of forest floor microbial communities than harvest or wildfire alone.

Distributed Adaptive Human-in-the-Loop Event-Triggered Formation Control for QUAVs With Quantized Communication

To improve the safety and reliability of quadrotor unmanned aerial vehicles (QUAVs) with limited communication, system uncertainties, and unknown external disturbances in a highly uncertain and safety-critical environment, a distributed adaptive human-in-the-loop (HiTL) event triggered (ET) formation controller is proposed. The non-autonomous leader are controlled by receiving control commands decided by the gesture recognition system, and the followers are controlled indirectly via the connected communication network. Thus, the safety and flexibility of the closed-loop system are improved. The designed controller is quantized and then sent to actuator only at the ET instants to further reduce the network burden. The radial basis function neural network (RBFNN) is used to approximate the system uncertainties. The unknown approximation error and the unknown external disturbance are viewed as a compound disturbance compensated by the high order disturbance observer (HODO). In addition, the uniformly ultimately bounded (UUB) stability of the closed-loop system is achieved through the Lyapunov method. Finally, comparative experiments are implemented on the QUAVs to demonstrate the validity of the presented control scheme.

Finite-time anti-disturbance control of an information-constrained autonomous helicopter flexible slung-load system with compound disturbance

This study investigates the finite-time anti-disturbance control problem of an autonomous helicopter flexible slung-load system (AHFSLS) by describing it as a distributed parameter system with matched disturbance, mismatched disturbance, and vibration information constraint. Here, initially, a novel compound disturbance observer is proposed to simultaneously compensate for matched and mismatched disturbances. Then, by executing a rigorous Lyapunov stability analysis, with the help of a barrier Lyapunov function, the vibration amplitude converges into a compact set for the AHFSLS. The simulation results demonstrated the efficiency of the developed control strategy in dealing with matched and mismatched disturbances, vibration information constraint, and vibration in the AHFSLS.

Disturbance observer-based fixed-time leader-following consensus control for multiple Euler–Lagrange systems: A non-singular terminal sliding mode scheme

This paper focuses on the fixed-time leader-following consensus problem for multiple Euler–Lagrange (EL) systems via non-singular terminal sliding mode control under a directed graph. Firstly, for each EL system, a local fixed-time disturbance observer is introduced to estimate the compound disturbance (including uncertain parameters and external disturbances) within a fixed time under the assumption that the disturbance is bounded. Next, a distributed fixed-time observer is designed to estimate the leader’s position and velocity, and the consensus problem is transformed into a local tracking problem by introducing such an observer. On the basis of the two types of observers designed, a novel non-singular terminal sliding surface is proposed to guarantee that the tracking errors on the sliding surface converge to zero within a fixed time. Furthermore, the presented control algorithm also ensures the fixed-time reachability of the sliding surface, while avoiding the singularity problem. Finally, the effectiveness of the proposed observers and control protocol is further verified by a numerical simulation.

Adaptive Learning Control of Switched Strict-Feedback Nonlinear Systems With Dead Zone Using NN and DOB.

This article investigates the adaptive learning control for a class of switched strict-feedback nonlinear systems with external disturbances and input dead zone. To handle unknown nonlinearity and compound disturbances, a collaborative estimation learning strategy based on neural approximation and disturbance observation is proposed, and the adaptive neural switched control scheme is studied in a dynamic surface control framework. In the adaptive learning control design, to obtain the evaluation information of uncertain learning, the prediction error is constructed based on the composite learning scheme. Then, the prediction error and the compensated tracking error are applied to construct the adaptive laws of switched neural weights and switched disturbance observers. The system stability analysis is carried out through the Lyapunov approach, where the switching signal with average dwell time is considered. Through the simulation test, the effectiveness of the proposed adaptive learning controller is verified.

Neural Output Feedback Control of Automobile Steer-by-Wire System With Predefined Performance and Composite Learning

This article addresses the steering control problem for steer-by-wire (SbW) systems subject to the unknown uncertainty, external disturbance and unavailable variable. Before the controller design, an adaptive neural network-based observer and a disturbance observer are constructed to estimate the angular velocity signal and the compound disturbance, respectively. Then, to guarantee the transient and steady-state performance of steering tracking error within the quantitative boundary, a prescribed performance function is constructed by user-designed tracking accuracy and settling time. Finally, the controller is designed based on the backstepping scheme and the neural network with a composite learning scheme is proposed for the approximation of lumped uncertainty. The Lyapunov stability theory shows that the signals involved in the system are semiglobal uniformly ultimately bounded and the tracking error converges to a preset range at finite time. Different numerical simulations and experiments are implemented to verify the effectiveness of the developed control scheme.

Adaptive Neural Control of Nonlinear Cyber–Physical Systems Against Randomly Occurring False Data Injection Attacks

In this article, the main contribution is to introduce the adaptive backstepping technique into nonlinear cyber–physical systems against randomly occurring false data injection (ROFDI) attacks. And a new defense strategy based on nonlinear disturbance observer (NDO) is developed, which can not only effectively estimate the external compound disturbance in the presence of the attack, but also improve the robustness of the controlled system. Different from FDI attacks, it is a special case of ROFDI attacks, and the proposed method can deal with ROFDI attacks injected by attackers. Meanwhile, multiple Nussbaum functions are introduced, which overcomes the design difficulty of unknown control directions caused by ROFDI attacks. Furthermore, the approximation of the unknown nonlinear function and the exponential growth problem in the traditional backstepping calculation process are handled by radial basis function neural network and dynamic surface control, respectively. Finally, a new adaptive neural control method based on NDO is proposed to make all signals bounded. Meanwhile, tracking errors and disturbance estimation errors converge on the neighborhood of zero. Numerical and practical examples further illustrate the rationality of the method.

Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

Increasing fire frequency may trigger eco‐hydrologic divergence

AbstractClimate‐induced fire regimes may change species abundance and species composition in affected forest types, potentially altering pyro‐eco‐hydrologic feedbacks. In some fire‐prone forests across the globe, eco‐hydrologic thresholds (changing points, or tipping points, in ecohydrology when vegetation shifts from one steady vegetation to another) are being exceeded due to changes in relationships between climate, fire and vegetation. Following compound disturbances, forests may fail to maintain ecological resilience. Under multiple burn conditions, Eucalyptus regnans F. Muell. forests in south east Australia are highly vulnerable to ecological tipping points. In Victoria, over 189 000 ha of obligate seeder forests have been burned two or more times within 18 years. These short return‐interval fires allow Acacia dealbata to become the dominant overstorey species. Such a dramatic species replacement may result in a new evapotranspiration (ET) regime, leading to a new hydrologic state. Stand scale dynamic models were combined with field estimated ET in E. regnans and A. dealbata forests aged 10, 35 and 75/80 years. We found that long‐term forest structure, ET and water yield significantly diverge between E. regnans and A. dealbata forests with increasing age. These divergences imply a non‐equilibrium state after A. dealbata replaces E. regnans under high‐frequency fire conditions. In senescing A. dealbata, understorey transpiration contribution of 29.8% to system ET was similar to that of overstorey transpiration (31.2%), indicating the understorey and overstorey contribute equally to total ET at the final stage of Acacia forests. In contrast, in 75‐year‐old E. regnans forests, understorey contribution to the total system evapotranspiration is about 16%. This suggests that, after the Acacia life cycle finishes, the ET regime will transit into a new state that will be dominated by shrubby understorey species. Our findings suggest that this climate‐induced species replacement would decrease long‐term ET, inferring an increase in streamflow.

Could biological soil crusts act as natural fire fuel breaks in the sagebrush steppe?

Could biological soil crusts act as natural fire fuel breaks in the sagebrush steppe?

Design of Predefined Time Convergent Sliding Mode Control for a Nonlinear PMLM Position System

The significant role for a contemporary control algorithm in the position control of a permanent magnet linear motor (PMLM) system is highlighted by the rigorous standards for accuracy in many modern industrial and robotics applications. A robust predefined time convergent sliding mode controller (PreDSMC) is designed for the high precision position tracking of a permanent magnet linear motor (PMLM) system with external disturbance, and its convergence time is independent of the system’s initial value and model parameters. We verified theoretically that the performance function conditions are satisfied, the motor speed is steady and constrained, and the motor position tracking error converges to zero within the prescribed time. First, we designed a sliding mode (SM) surface with predetermined time convergence, which mathematically demonstrates that the tracking error converges to zero within the predefined time and shows that the position tracking accuracy is higher. Secondly, we developed a PreDSMC law that is independent of initial state and based on the predefined time convergence Lyapunov stability criterion. Finally, to prove the accuracy and higher precision of the proposed PreDSMC, comparative numerical simulations are performed for PMLM with compound disturbances. Simulation findings show that the suggested robust predefined control method considerably reduces the impacts of friction and external disturbances; consequently, it may increase the control performance when compared to the typical proportional integral derivative (PID) controller, the nonsingular fast terminal SMC, and the linear SMC.

A novel fixed‐time sliding mode control for nonlinear manipulator systems based on adaptive disturbance observer

AbstractConsidering that in the trajectory tracking control of a nonlinear robotic manipulator system, the control effect is easily limited by the initial state of the system, and the system has modeling error, unknown disturbance, and friction in the actual control, to overcome the above problems, a fixed‐time sliding mode control (SMC) strategy based on adaptive disturbance observer (ADO) is developed in this paper. Firstly, feedforward compensation of the system is achieved by developing an ADO to accurately estimate the compound disturbances in the system. Second, a new fixed‐time sliding mode (SM) surface is presented to overcome the singularity issue and accelerate the error convergence. In addition, to enhance the performance of the reaching phase, a variable exponential power reaching law (VEPRL) is developed, which can effectively adjust the convergence rate. Through rigorous theoretical analysis, it is shown that the system state can be stabilized at a fixed time, and an upper bound on the convergence time is also given. Finally, the effectiveness of the control method is verified by comparing it with different control schemes in simulation.