Multiple External Disturbances Research Articles

Study on Factors Influencing Forest Distribution in Barcelona Metropolitan Region

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of an interactive assessment of their distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest area but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of the forests has been increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Emergence of Adaptation of Collective Behavior Based on Visual Perception

Unmanned swarms are widespread used in the IoT. The ability of unmanned swarms to achieve adaptive collective behavior in complicated mission scenarios is a prerequisite for meeting mission objectives. However, classical collective behavior models often use the velocity and position of neighbors as inputs to be constructed from a phenomenological perspective. This complicates the construction of unmanned swarms and is incompatible with biological perception. Therefore, this paper proposes an Observation-Orientation-Decision-Action (OODA) framework for the construction of adaptive collective behavior based on visual perception, inspired by biological collective behavior formations. The model contains no explicit alignment, and no information exchange occurs between individuals. Instead, individuals make decisions based purely on the sight distance corresponding to different relative orientations. Based on adaptability evaluation metrics defined at the collective level, experiments including coordinated collective motion, single disturbed individual, single external disturbance, narrow passage, and multiple external disturbances scenarios show that the group can respond adaptively to different scenarios with a stable crystal structure while avoiding collisions. In addition to particle simulations, different scenarios provide validation using the Webots robot simulator. As a result, this approach compensates for the inadequacies of existing models and provides technical support for the application of unmanned swarms in various IoT scenarios.

Semi-active fuzzy cooperative control of vehicle suspension with a magnetorheological damper

This study attempts to improve the vibration isolation performance of a vehicle suspension system with a magnetorheological damper (MRD) under complex driving conditions. Structure parameter uncertainty, disturbance of the driving process, and response time delay of MRD are all addressed. Firstly, experiments of MRD were carried out in a damping force testing machine to identify the parameters of the MRD adjustable Sigmoid model by the Levenberg-Marquardt optimization algorithm. Then, the parameter identification is verified by comparing experimental and simulation data. Secondly, the state space equations of the suspension system are derived by Newton’s second law. The transfer function from the bounded disturbance input to the control output is obtained based on H∞ control theory. To make the Infinite norm of the system transfer function less than a certain value, three control strategies are proposed: variable structure control (VSC), disturbance rejection control (DRC), and delay tolerance control (DTC). Thirdly, considering these issues together to weaken the effect of disturbances on vehicle driving conditions, a fuzzy cooperative control (FCC) strategy is proposed based on the linear matrix inequality (LMI) theory. Simulation results demonstrate that FCC semi-active vehicle suspension systems conduct effective vibration isolation performance while responding to multiple external disturbances.

Variations in fish community composition and trophic structure under multiple drivers in the Beibu Gulf

IntroductionBeibu Gulf is a semi-enclosed bay with important ecological and economic value of the northwestern South China Sea (SCS). It proved to be a stressed ecosystem and therefore clearly vulnerable to further external disturbances.MethodsData from 26 fishery bottom trawl surveys in the Beibu Gulf from 2006 to 2018 are analyzed to reveal changes in the fish abundance, dominant species composition, diversity, mean trophic level (MTL), and fishing-in-balance (FiB) index.ResultsFish abundance decreased significantly (p< 0.05) with increased anthropogenic disturbance (e.g., fishing intensity and land-derived pollutants), with catch per unit effort (CPUE) decreasing from 40.69 kg·h−1 in 2006 to 15.84 kg·h−1 in 2018. Dominant species composition has changed dramatically from 2006 to 2018, with fish communities shifting from demersal to pelagic species, and from large and high-trophic-level species to small and low-trophic-level species. Meanwhile, Margalef’s richness and Shannon–Wiener diversity indexes trend downward. The MTL declines from 3.82 in 2006 to 3.71 in 2018 (at 0.08 trophic level per decade), and decreasing with the decrease of the proportion of high-trophic fish and demersal fish in total catch (p< 0.05). A FiB index tends to be less than 0, this index declines with decreasing MTL and fish abundance (p< 0.05).DiscussionOverall, fish stocks in the Beibu Gulf continue to decline and are currently overexploited. Multiple external disturbances, such as fishing (including overfishing, dominance of trawl fishery, and ‘skipper effect’), habitat disturbance, pollution, and temperature changes, may have contributed to significant changes in fish communities and trophic structure in the Beibu Gulf.

A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.

Urban heat vulnerability: A dynamic assessment using multi-source data in coastal metropolis of Southeast China.

Extreme heat caused by global climate change has become a serious threat to the sustainable development of urban areas. Scientific assessment of the impacts of extreme heat on urban areas and in-depth knowledge of the cross-scale mechanisms of heat vulnerability forming in urban systems are expected to support policymakers and stakeholders in developing effective policies to mitigate the economic, social, and health risks. Based on the perspective of the human-environment system, this study constructed a conceptual framework and index system of "exposure-susceptibility-adaptive capacity" for urban heat vulnerability (UHV) and proposed its assessment methods. Taking Xiamen City, a coastal metropolis, as an example, spatial analysis and Geodetector were used to explore the spatial and temporal changes, spatial characteristics, and patterns of UHV under multiple external disturbances from natural to anthropological factors, and to reveal the main factors influencing UHV forming and spatial differentiation. Results showed that the exposure, susceptibility, adaptive capacity, and UHV in Xiamen City had a spatial structure of "coastal-offshore-inland". On the hot day, both the exposure and UHV showed a temporal pattern of "rising and then falling, peaking at 14:00" and a spatial pattern of "monsoonal-like" movement between coast and inland. Coastal zoning with favorable socioeconomic conditions had less magnitude of changes in UHV, where the stability of the urban system was more likely to be maintained. During the hot months, the high UHV areas were mainly distributed in the inland, while coastal areas showed low UHV levels. Further, coastal UHV was mainly dominated by "heat exposure", offshore by "comprehensive factors", and inland in the northern mountainous areas by "lack of adaptive capacity". Multi-scale urban adaptive capacity was confirmed to alter spatial distribution of exposure and reshape the spatial pattern of UHV. This study promotes the application of multi-scale vulnerability framework to disaster impact assessment, enriches the scientific knowledge of the urban system vulnerability, and provides scientific references for local targeted cooling policy development and extreme heat resilience building programs.

Mapping the Spatiotemporal Pattern of Sandy Island Ecosystem Health during the Last Decades Based on Remote Sensing

Sandy islands suffer multiple external disturbances and thus experience drastic temporal ecological changes, and the two parts, that is, the states of essential components (Part 1) and the resilience under multiple disturbances (Part 2), are both indispensable to the sandy island ecosystem health. In this study, a model for the sandy island ecosystem health was established by integrating the two parts. In Part 1, the states were measured following the framework of vegetation, soil, and landscape, and a total of 12 factors in the three components were adopted. In Part 2, three typical disturbances, namely, geomorphological change, soil salinization, and human influence, were identified, and the resilience across different time intervals was measured by clarifying the intrinsic correlations between the components and disturbances. A sandy island ecosystem health index (SIEHI) was proposed based on the two parts, and Chongming Island was selected as the study area to demonstrate the model. The results indicated that the SIEHI continuously increased from 1988 to 2017, denoting the good effects of “Eco-Island construction” on Chongming Island. In different components and factors, the vegetation and soil components contributed more than the landscape component to the sandy island ecosystem health, and vegetation quality and soil carbon/nitrogen were the factors that made the most contributions. In different disturbances, the human influence played a major role in driving the spatiotemporal variations of the sandy island ecosystem health. Farming and building construction contributed the most and accounted for 37.12% and 35.59% of the total human influence, respectively, while traffic development exerted the highest influence per area. Then, influence coefficients of different human activities on the sandy island were determined, and measures for different functional zones were proposed for balancing the protection and development and achieving the sandy island ecosystem-based management.

Robust Stabilization of Underactuated TORA System Based on Disturbance Observer and Fixed-Time Sliding Mode Control Method

A translational oscillator with a rotational actuator (TORA) is an underactuated nonlinear mechanical system with two degrees of freedom (DOF). This paper concerns the robust stabilization control problem for the system with multiple external disturbances. First, a disturbance observer is constructed based on the internal nonlinear dynamic behavior of the system. Second, a robust stabilization controller is designed by the estimated disturbances and the fixed-time sliding mode control method. The controller realizes the global robust stabilization control objective of the TORA system, and the stability of both disturbance observer and robust closed-loop control system are analyzed using the Lyapunov theorem. Finally, the effectiveness of the theoretical results are verified by numerical experiments.

Dynamic Balancing of Humanoid Robot with Proprioceptive Actuation: Systematic Design of Algorithm, Software, and Hardware.

For humanoid robots, maintaining a dynamic balance against uncertain disturbance is crucial, and this function can be achieved by coordinating the whole body to perform multiple tasks simultaneously. Researchers generally accept hierarchical whole-body control (WBC) to address this function. Although experts can build feasible hierarchies using prior knowledge, real-time WBC is still challenging because it often requires a quadratic program with multiple inequality constraints. In addition, the torque tracking performance of the WBC algorithm will be affected by uncertain factors such as joint friction for a large transmission ratio proprioceptive-actuated robot. Therefore, the balance control of physical robots requires a systematic solution. In this study, a robot control system with high computing power and real-time communication ability, UBTMaster, is implemented to achieve a reduced WBC in real time. Based on these, a whole-body control scheme based on task priority for the dynamic balance of humanoid robots is implemented. After realizing the joint friction model identification, finally, a variety of balancing scenarios are tested on the Walker3 humanoid robot driven by the proprioceptive actuators to verify the effectiveness of the proposed scheme. The Walker3 robot exhibits excellent balance when multiple external disturbances occur simultaneously. For example, the two feet of the robot are subjected to tilt and displacement perturbations, respectively, while the torso is subjected to external shocks simultaneously. The experimental results show that the dynamic balance of the robot under multiple external disturbances can be achieved by using strictly hierarchical real-time WBC with a systematic design.

Model‐free adaptive control based on switching mechanism for multi‐input multi‐output wet flue gas desulfurization system

SummaryThe wet flue gas desulfurization (WFGD) process is a complex controlled system with nonlinear and time‐varying characteristics. There are a variety of external disturbances in the desulfurization process, which can easily cause a jump in system parameters and even structure. In response to this problem, a model‐free adaptive control scheme based on a switching mechanism is proposed, and the convergence analysis is given. In the scheme, firstly, the operating conditions of the WFGD system are divided into n kinds based on multiple external disturbances, and the mathematical models of the multi‐input multi‐output WFGD system under different operating conditions are established. Then the MFA controllers of the WFGD system under multiple operating conditions are established, respectively. Finally, a switching index function is designed to switch the controller of the WFGD system according to different operating conditions, so that the desulfurization system is always controlled by the most appropriate controller. In the simulation experiment, according to the actual data of the WFGD system of a 1000 MW coal‐fired power station in Beijing, the modeling test and algorithm simulation of the WFGD system under three operating conditions are carried out. The simulation results show that this method can effectively reduce the transient error of the WFGD system output and improve the desulfurization effect.

Dynamic Modeling and Robust Adaptive Sliding Mode Controller for Marine Cable-Driven Parallel Derusting Robot

Ship derusting has the characteristics of a complex operation environment, high labor intensity and low efficiency. In order to better cope with this situation, a new type of cable-driven parallel derusting robot (CDPDR) is proposed in this article. To improve the positioning accuracy and anti-interference capacity of the motion platform where the end effector is mounted, the system’s dynamic model, considering wave excitation, is established. Further, the controllable workspace and cable tension optimization algorithm are studied. In addition, a fast non-singular terminal sliding-mode controller is designed. Meanwhile, the adaptive technique is used to estimate the disturbance upper bound. Then, the Lyapunov theory is applied to prove the stability of the system. Finally, the performance of the controller is verified by high-fidelity simulations in two different scenarios. The results show that the proposed controller can converge in finite time and maintain small error under multiple external disturbances. The relevant research in this article can provide theoretical guidance for the application of CDPDRs on ships.

Anti-disturbance composite tracking control for a coupled two-tank MIMO process with experimental studies

Coupled multiple-tank systems are very important for a wide range of industrial applications due to their unique uses. However, the liquid level control for the coupled two-tank multi-input multi-output (MIMO) system is quite challenging because it has strong nonlinearity and coupling, and it is susceptible to multiple external disturbances. For this process, this paper proposes a novel anti-disturbance control strategy consisting on a nonlinear composite hierarchical anti-disturbance predictive control (CHADPC). First, a model-based explicit nonlinear model predictive controller (ENMPC) is designed assuming that all disturbances are measurable and its global exponential stability is proved. Then, a nonlinear disturbance observer (DO) is designed to estimate the lumped disturbances. The composite controller handling the estimated disturbances is then proposed. Finally, simulation and experimental tracking control tests under perturbations and comparisons with recently reported works have been carried out to highlight the promising performance of the proposed ENMPC and CHADPC schemes.

Robust adaptive attitude control of flexible spacecraft using a sliding mode disturbance observer

A composite control scheme based on a disturbance observer is presented in this paper to deal with the attitude control problem of flexible spacecraft. Specifically, a sliding mode disturbance observer (SMDO) is developed to attenuate the unmodeled system dynamics, parameter uncertainties, and multiple external disturbances. The key feature of this approach is to relax the assumption imposed on disturbance to be constant or changing at a slow rate, which is a typical assumption in this class of problems involving a disturbance observer. Then, an exclusive adaptive integral sliding mode controller is combined with the SMDO to get the improved closed-loop control performance of the system. The underlying benefit of the proposed control scheme is improved robustness against time-dependent external disturbances and system model uncertainties. The stability analysis of the closed-loop system is provided using Lyapunov’s stability theory. Simulation results are provided to show the effectiveness of the proposed control scheme, especially in comparison with existing results.

Fault Diagnosis of HVAC System Considering LMBP Neural Network Method

Given the complexity of the mathematical model for the HVAC system and the constant changes of multiple external disturbances and working conditions, the application of intelligent control in the HVAC control system to improve the control performance of the HVAC control system has become a research hotspot. The Levenberg-Marquart algorithm (LM algorithm) is studied to address the defects of slow convergence speed and prone to falling into local minimum in the learning process of the BP neural network. To solve the two issues (the choice of the learning rate, and the solution of the inverse matrix) that significantly affect the training time and convergence accuracy, we improved and optimized the LM algorithm using the LU decomposition method and implemented the algorithm through the MATLAB programming language. The LMBP neural network method obtained is applied to the control loop of the HVAC chilled water cycle. The simulation comparison study is performed on its control effect and that of the PID control algorithm and BP neural network PID control algorithm. The research results show that the LMBP neural network method has significantly improved the performance in aspects such as reducing overshoot, accelerating convergence speed and reducing steady-state error, etc.

Method of Multiobjective Optimization of Controlled Systems with Distributed Parameters

The constructive method for multicriteria optimization of control processes of deterministic and not fully defined controlled systems with distributed parameters, described by linear multidimensional parabolic partial differential equations with internal and boundary control actions, is proposed. The optimization problem is considered when an uniform approximation accuracy of object’s final state to the required spatial distribution of controlled function is given. The suggested approach is based on the one-criterion option in the form of minimax convolution of the normalized quality criteria and the subsequent transition to the equivalent form of a typical variational problem with constraints. It is applied to the deterministic model of an object described by an infinite system of differential equations with respect to time-dependent modes of the controlled quantity expansion in a series of eigenfunctions of the initial-boundary value problem. Further procedures for the preliminary parametrization of control actions, based on analytical optimum conditions and reduction to semi-infinite programming problems, allow one to find the desired extremals using their Chebyshev properties and fundamental laws of the domain in typical application conditions of estimating the accuracy of approaching the object’s final state to the required one in a uniform metric. The obtained results are extended to the tasks of program control on the principle of guaranteed result by ensembles of object trajectories under conditions of interval uncertainty of the parametric characteristics of the distributed system and multiple external disturbances. A demonstrated example of a multicriteria optimization of an innovation technology of metal induction heating prior a hot forming is of special interest. The typical optimization criteria such as energy consumption, metal loss due to scale formation, and heating accuracy are considered as components of vector optimization criterion.

Multiplicative Stochastic Systems with Multiple External Disturbances

With the methods of H2/H∞-control theory, in the presence of noise we solve the optimization problem for a multiplicative stochastic system with several external disturbances (the multiperturbation problem) and vector Wiener processes with arbitrary intensity matrices. We obtain matrix differential equations of Riccati type, reducing the original optimization problem to solving these equations.

Performance of a neural adaptive tracking controller for multi-input nonlinear dynamical systems in the presence of additive and multiplicative external disturbances

We discuss the tracking problem in the presence of additive and multiplicative external disturbances, for affine in the control nonlinear dynamical systems, whose nonlinearities are assumed unknown. Based on a recurrent high order neural network (RHONN) model of the unknown plant, a smooth control law is designed to guarantee the uniform ultimate boundedness of all signals in the closed loop. Certain measures are utilized to test its performance. The controller, which can be viewed as a nonlinear combination of three high order neural networks, does not require knowledge regarding upper bounds on the optimal weights, modeling error and external disturbances. Simulations performed on a simple example illustrate the approach.