Presence Of Severe Disturbances Research Articles

Model-based and model-free position control of a servo pneumatic system in the presence of uncertainties and disturbances

Trajectory tracking in electro-pneumatic systems poses a significant challenge due to the nonlinearity of their dynamics. This research aims to analyze the performance of various types of controllers for a laboratory-scale pneumatic system in terms of energy consumption and control efforts. By considering the mathematical relationships among system components, a precise gray-box mathematical model is developed based on experimental data. Subsequently, different linear model-free and nonlinear model-based controllers, is designed and their performances are compared by measuring tracking error, control effort, maximum overshoot, settling time, robustness against disturbances, and energy efficiency in the presence of severe disturbances. The effectiveness of these control methods is assessed through experimental tests and compared.

Anti-disturbance attitude control for quadrotor unmanned aerial vehicle manipulator via fuzzy adaptive sigmoid generalized super-twisting sliding mode observer

During the flight of quadrotor unmanned aerial vehicle manipulator, external wind and model uncertainties will significantly affect the accuracy and stability of the controller. This study investigates the problem of high-precision attitude control for the quadrotor manipulator that is equipped with a 2-DOF robotic arm in presence of several disturbances based on a new sliding mode observer and a corresponding sliding mode controller. As for the proposed sliding mode observer, to reduce its estimation chattering phenomenon, a sigmoid function is exploited to replace the discontinuous signum function. Moreover, to adapt to the estimation of disturbances with different upper bounds of the first derivative, a fuzzy logic system algorithm is used to adaptively update the observer gains and slope parameters of the sigmoid function. Furthermore, a generalized super-twisting algorithm is incorporated into the proposed sliding mode observer. Similarly, the sliding mode controller is constructed by using the generalized super-twisting algorithm and a sigmoid function in addition to the sliding mode observer-based feedforward disturbance compensation. In addition, to further relieve the influence of system sensor noise, a tracking differentiator is exploited to incorporate with both proposed sliding mode observer and sliding mode controller. Finally, to demonstrate the effectiveness of the proposed method, several simulations and experiments on the quadrotor unmanned aerial vehicle manipulator system are conducted under varying external disturbances.

Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process

In this contribution, a data-driven control approach was developed and applied experimentally to a pharmaceutical batch cooling crystallization process. In this approach, a radial basis functions (RBF) network model was trained in real-time with experimental data (time varied temperature and chord length distribution) with two different input data update strategies. The control objective was to optimize the cooling profile with the aid of trained RBF to achieve the desired crystal population profile throughout the process. The robustness of the proposed control strategy was tested with 10 comprehensive experiments in the presence of several disturbances (initial supersaturation, impeller speed, water composition and seed size). The presented control strategy was able to easily handle all the case scenarios. In 8 cases, the experimental crystal population profile followed successfully the reference with less than 10% offset. In the remaining 2 cases, the offset was 17% that was due to the absence of the supersaturation. The proposed RBF network-driven control is a promising strategy that is easy to implement, fully-automated and relies on relatively limited data for training. Therefore, the RBF control is expected to contribute to quick process development and control, especially when there is a lack of comprehensive process understanding and historical data especially in the pharmaceutical industry.

Forecasting intensifying disturbance effects on coral reefs.

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal.

Woody species-specific disturbance regimes and strategies in mixed mountain temperate forests in the Šumava Mts., Czech Republic

The disturbance regime of mountain spruce–beech temperate forests has not yet been sufficiently elucidated. We hypothesized that spruce and beech express completely different disturbance histories and behavioural strategies, potentially causing exceptionally complex disturbance regimes. We further hypothesized that the spontaneous development of mountain forests can temporarily result in a simplification of the forest's spatial structure. We wanted to discover how the disturbance history and growth plasticity of the main tree species differ, and whether some old managed forests arose from primeval forest remnants. We compared dendrochronological records of the unmanaged Boubin Primeval Forest and 30 sites with current forestry records. Using this comparison, we categorized all sites into three categories. In the disturbance history of all evaluated forest sites, there was clear evidence of the presence of severe disturbances in the nineteenth century. However, the regeneration of beech was more continuous and less dependent on the presence of severe disturbances than the regeneration of spruce, which depended on the presence of severe disturbances of low frequency. Human-induced changes at some sites were manifested in changes in the initial growth of both species and disrupted their mutual competition and also led to a higher growth plasticity of beech. Despite human impacts in the region since the end of the nineteenth century, about 30% of analysed trees were older than the severe disturbances in the nineteenth century; therefore, some studies sites preserved the characteristics of primeval forest. Our results revealed three main forest development trajectories since the end of the nineteenth century.

Supervisory control of an anaerobic digester subject to drastic substrate changes

Anaerobic digestion (AD) is a green technology that has been applied for many years. One of the main problems in this process is controlling these bioreactors to maximize methane production. A supervisory control strategy has been proposed to improve the methane production rate in an anaerobic digestion process while minimizing the risk of process failure in the presence of several drastic feedstock changes. The inner loop consisted of a feedback control that manipulated the feed flow rate for achieving the desirable methane production rate. A rule based control strategy was used as supervisory control loop. This controller received the total volatile fatty acids concentration in the reactor and the trends of methane production rate to calculate the set-point of the inner control loop. pH was used as a safety control parameter to prevent the system from acidification. The proposed control scheme was tested in the presence of several disturbances in the feedstock, including glucose overload, ammonia inhibition, substrate dilution and shifting feeding from manure to bio-pulp. The controller prevented VFA accumulation above 2.73, 2.94 and 4.09 g L−1, under glucose overload, ammonia inhibition and change of feedstock to bio-pulp, respectively. Thus, the supervisory control could successfully improve the methane production rate and keep the reactor stable against external severe disturbances.

Fast robust power system dynamic state estimation using model transformation

Accurate information about generator rotor speeds and angles plays an important role for power system transient stability online assessment and protection. To address this need, this paper proposes a fast and robust estimation approach based on the model transformation strategy. Thanks to this strategy, the original complex nonlinear model is transformed into a linear one without linerization, which makes the dynamical system observability analysis and the estimation problem significantly easier to solve. The proposed model transformation strategy is achieved by taking the measured generator active power as the input variable and the derived frequency and the rate of change of frequency measurements from the phasor measurement units (PMUs) as the output variables of the dynamical generator model. This allows us to estimate the generator rotor speeds and angles using only local PMU measurements and the swing equations, relaxing the need of a detailed generator model on which the existing dynamic state estimators are based. A robust Kalman filter is also developed to handle data quality problems as the frequency and rate of change of frequency measurements can be biased in presence of severe disturbance or communication issues. Comparison results carried out on the IEEE 39-bus system successfully validate the effectiveness and robustness of the proposed approach under various conditions.

Small-signal modeling and multivariable PI control design of VSC-HVDC transmission link

This paper employs a multivariable PI (proportional-integral) controller for inner-loop control and SISO (single-input single-output) PI controllers for outer-loop control for a VSC-HVDC (voltage source converter-based high voltage direct current) transmission system. To design the control system, first, a state-space model of VSC-HVDC transmission link is obtained in dq-coordinate frame. Then, using linearization technique the small-signal model is obtained. Next, a multivariable optimal PI controller is designed for the (inner-loop) linearized model using LQR (linear quadratic regulator) approach. Finally, the outer-loop SISO PI controller parameters are obtained using sequential design method (i.e., assuming that the inner-loop is must faster than the outer-loop). The advantage of using the MIMO (multi-input multi-output) PI controller over the conventional decouple-based PI controller for inner-loop control is studied through different performance and robustness measures, and also verified through simulations in presence of several disturbances and parameter variations.

A Complementary SVC-Based Controller Design for Damping Both Local and Inter-Area Oscillating Modes Using NSGA-II Algorithm

Static Var compensator (SVC) is one of flexible AC transmission system (FACTS) elements mainly used for reactive power and voltage control in power systems. This paper deals with multi-modal electromechanical oscillations damping in the presence of severe disturbances. These oscillations include local modes, inter-area modes and inter-plant modes. To enhance the damping of the oscillations, the complementary controller is added to voltage regulator of SVC. In order to design such a controller, as a significant contribution of this paper, either of oscillating modes is considered as a distinct objective function and a multi-objective optimization model is employed to improve all these oscillating modes simultaneously. Accordingly, in this paper, the non-dominated sorting genetic algorithm-II (NSGA-II) approach is used to optimize all the parameters of complementary controller with the aim of minimizing all three objective functions. By applying NSGA-II method 10 non-dominated solutions are generated, and a fuzzy method is utilized to select the best compromise solution among the produced solutions. The well-tuned complementary SVC-based controller is examined on a two area power system and results obtained through simulating different cases illustrate the robustness and the efficiency of the proposed controller.

Autolanding Control Using Recurrent Wavelet Elman Neural Network

This paper develops a control system with the recurrent wavelet Elman neural network (RWENN) that improves the capabilities of a commercial aircraft to land automatically (autoland) when it is subjected to severe wind disturbances and faults. The proposed RWENN controller is used for the autolanding control, as its real-time learning ability is better than a conventional neural network. The parameters of the RWENN are: 1) translations and dilations of the hidden layer’s wavelet functions and 2) the weights between the hidden and output layers. These parameters are learned online using the gradient descent method. The adaptive laws of learning rates are derived from the Lyapunov theorem; hence, system stability can be guaranteed. Moreover, optimal learning rates provide the fastest convergence of parameters. Simulation results show that the RWENN-based control scheme can achieve better performance than other control schemes for the autolanding system in the presence of severe disturbances and faults.

Plantwide Control of Biodiesel Production from Waste Cooking Oil Using Integrated Framework of Simulation and Heuristics

This article describes the systematic plantwide control (PWC) design of an ecofriendly process for biodiesel production from waste cooking oil (WCO) feedstock. A simulation model was developed to produce biodiesel from WCO that reduces both production costs and adverse environmental impacts. An effective PWC system is crucial for the safe, smooth, and economical operation of a biodiesel plant. Hence, a PWC system was developed for a homogeneously catalyzed biodiesel process using the integrated framework of simulation and heuristics (IFSH). The main merits of the IFSH methodology are effective use of rigorous process simulators and heuristics in developing a PWC system and simplicity of application. Finally, the performance of the developed control system was assessed in terms of settling time, a dynamic economic index based on the deviation from the production target (DPT), and the overall total variation (TV) in manipulated variables. These performance assessments and the results of dynamic simulations showed that the developed PWC system is stable, effective, and robust in the presence of several disturbances and that biodiesel quality can be maintained despite these disturbances. This is the first study to develop a complete PWC system for a homogeneously catalyzed two-step biodiesel production from WCO.

SLIDING MODE CONTROL FOR AUTOMOBILE AIR CONDITIONER

Increasing demand toward driving comfort steers automobile engineers to spend more and more effort on refining the automobile air conditioning system. The system is of high order and operated at the presence of several disturbances. The control algorithm should be able to control the evaporator out temperature with the acceptable control action. In this work, the sliding mode control is adopted for its advantage of dealing with high order, nonlinear system. Besides, several linear and nonlinear observers are proposed such that the controller may be implemented under the constrain of limited sensor set.