Changes In Plant Dynamics Research Articles

Everyone makes mistakes: Sampling errors in vegetation analysis - The effect of different sampling methods, abundance estimates, experimental manipulations, and data transformation

Understanding of causes for recent changes in vegetation structure and species richness of natural habitats is crucial for their maintaining for future generations. However, to avoid misinterpretation of vegetation changes in time, we should be aware of limits and errors of methods used for vegetation sampling. In a specific vegetation type, i.e. species rich wet meadow, we quantified sampling error in vegetation sampling at four different sampling levels (visual cover estimation, detailed recording in a grid of small cells, detailed assessment during clipping for biomass, biomass sorting), compared differences among three abundance estimates (frequency, cover and biomass), and assessed the effect of data transformation, and rapid change in vegetation structure caused by experimental species removal. At the 1 m2 scale the captured proportion of species missed by classical relevé sampling was on average 16%. Subsequent detailed subquadrat sampling captured the majority of previously overlooked species. The chance of a species being overlooked increased both with rarity, and the species richness of the area sampled. Where abundance was measured using metrics of cover and biomass, common species were overvalued, but when abundance was measured using frequency, common species were undervalued. In this study, logarithmic transformation of values provided a more reliable characterization of vegetation, than binarized or untransformed values. With the exception of species abundance, the number of species overlooked, quadrat species richness, and vegetation characterization were all affected by the experimental treatment. Our findings highlight the potential effects of error when conducting vegetation sampling and analyses of community dynamics. Due to these effects, we need to consider the reliability of conclusions drawn when assessing temporal changes in plant dynamics. Data transformation modifies the effect of sampling error in analyses of vegetation data.

Reinforcement Learning and Deep Neural Networks for PI Controller Tuning

Abstract Reinforcement Learning, using deep neural networks, has recently gained prominence owing to its ability to train autonomous agents that have defeated human players in various complex games. Here, Reinforcement Learning is applied to the challenge of automatically tuning a proportional-integral controller, given only the process variable, set-point, manipulated variable and prior controller gains. The training considers random changes in plant dynamics, disturbances and measurement noise. Two training procedures were tested in this work, one that built up the difficulty of the simulation over time, and another that used the full complexity of the simulation throughout the training. The results show that building up the difficulty of the simulation by introducing greater degrees of randomness as the training progresses, produces an agent that is better able to tune the controller in question. Additional experience gathered in completing this work is also discussed to enable the reader to avoid some of the challenges encountered.

Neighbour effects on shrub seedling establishment override climate change impacts in a Mediterranean community

AbstractQuestionsCan theory about plant–plant interactions along climate gradients help us predict how climate change will affect shrub establishment? How does a predicted reduction in rainfall affect the outcome of the interactions between shrub seedlings and herbaceous plants in a dryland ecosystem?LocationThree field sites along a rainfall gradient and an adjacent experimental site at the Botanical Gardens of Tel Aviv University, Israel.MethodWe measured seedling survival of a dominant shrub species in response to the presence of herbaceous neighbours and to rainfall amount using two approaches: First, we employed a space‐for‐time approach and studied seedling survival along an aridity gradient that was shown to mimic the predicted rainfall changes. Second, we monitored seedling survival in a common garden experiment in which we simulated drought using rainout shelters. In both experiments we contrasted the importance of water availability relative to the presence of herbaceous neighbours and the origin of the shrub seedlings.ResultsOur results showed that neighbours always had a negative effect on shrub seedling survival. In contrast to theoretical predictions, the intensity of competition increased with reduction in water availability. Shrub seedlings originating from drier conditions showed higher survival than seedlings from mesic origin.ConclusionsThese results emphasize the importance of incorporating biotic interactions in studies predicting changes in plant dynamics under climate change. Moreover, plant species growing in ecosystems prone to high rainfall variability showed a greater ability to cope with dry conditions, which suggests more nuanced origin‐specific predictions than suggested by recent climate models.

Nonlinear continuous-time generalized predictive control of solar power plant

This paper presents an application of nonlinear continuous-time generalized predictive control (GPC) to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A brief description of the solar power plant and its simulator is given. After that, basic concepts of predictive control and continuous-time generalized predictive control are introduced. A new control strategy, named nonlinear continuous-time generalized predictive control (NCGPC), is then derived to control the process. The simulation results show that the NCGPC gives a greater flexibility to achieve performance goals and better perturbation rejection than classical control.

An autonomous approach for driving systems towards their limit: an intelligent adaptive anti-slug control system for production maximization

Anti-slug control in multiphase risers involves stabilizing an open-loop unstable operating point. Existing anti-slug control systems are not robust and tend to become unstable after some time, because of inflow disturbances or plant dynamic changes, thus, requiring constant supervision and retuning. A second problem is the fact that the ideal setpoint is unknown and we could easily choose a suboptimal or infeasible operating point. In this paper we present a method to tackle these problems. Our complete control solution is composed of an autonomous supervisor that seeks to maximize production by manipulating a pressure setpoint and a robust adaptive controller that is able to quickly identify and adapt to changes in the plant. The supervisor is able to automatically detect instability problems in the control loop and moves the system to a safer, stable operating point. Our proposed solution has been tested in a experimental rig and the results are very encouraging.

PSO Tuned PID-Based Model Reference Adaptive Controller for Coupled Tank System

In process industries control of nonlinear processes like level process is a common problem. For these nonlinear processes, to have a good performance even in the presence of environmental changes, the controller must have the ability to adopt changes in plant dynamics. So a Model Reference Adaptive Control (MRAC) based on PID controller is designed. Main objective of this work is to design PID-MRAC to include adaptiveness and to give good steady-state and transient performance for coupled tank system both with interaction and without interaction. A Particle Swarm Optimization (PSO) algorithm is used to fine tune the PID controller parameters. Simulations are done to show the effective performance of PID-MRAC compared to simple MRAC applied to couple tank system.

A switching control strategy applied to a solar collector field

This work presents a new switching control procedure that has been chosen to deal with the changes in plant dynamics. Several control systems composed of IMC-based PID controllers and feedforward compensators are designed for each operation region and a continuous switching mechanism for the overall control system is defined. Experimental tests which have been performed in a distributed collector field of a solar air cooling system, are presented showing promising results for the proposed strategy.

Closed-loop model validation based on the two-model divergence method

This paper is concerned with model validation and detection of parameter changes under closed-loop conditions. Two closed-loop model validation algorithms are developed based on the two-model divergence method. The algorithms are only sensitive to the plant changes that affect closed-loop performance. The first algorithm is sensitive to changes in both plant and disturbance dynamics. A novel feature of the second algorithm is that it is only sensitive to the changes in plant dynamics, regardless of changes in disturbance dynamics. Under certain conditions, the proposed algorithms can also be applied for fault detection, e.g. detection of actuator or sensor fault, etc. The developed algorithms are evaluated by simulations as well as experimental applications on a pilot scale process.

Robust control of the distributed solar collector field ACUREX using MPC for tracking.

This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint.

Development and application of a lyapunov synthesis based neural adaptive controller

The focus of this paper is to describe a neural adaptive control (NAC) technology derived using a Lyapunov synthesis technique. The NAC is a nonlinear adaptive controller which requires minimal plant information. It adapts its gains in real time to maintain the desired performance and to automatically compensate for changes in plant dynamics caused by system failures, environmental changes, or component damage. As such, the NAC control technology has the potential to enhance system performance by automatically optimizing its control laws for each operating regime. It can also increase reliability by automatically compensating for plant damage and system failures.

Gain-Scheduled H.INF. Control of a Pneumatic Serve System for Compensating Nonlinear Friction Force.

Friction force that pneumatic servo systems inherently possess varies considerably while the system is working. Therefore, changes in plant dynamics are inevitable. In addition, due to its nonlinearity and negative velocity-characteristics, stick-slip often occurs near a target position. This paper proposes gain-scheduled H∞ control of a pneumatic servo system with linear matrix inequalities (LMI) for compensating the influence of dynamic friction force on control performance. In the proposed approach, a linear parameter-varying (LPV) system is constructed by modeling a coefficient of dynamic friction, which varies under operation, as a time-varying parameter. The coefficient of dynamic friction is on-line estimated using the friction force model that we have previously proposed. In order to reduce deterioration of the control performance and avoid stick slip, controller dynamics is adjusted based on the estimation as it can follow the plant dynamics. The effectiveness of the proposed control system is experimentally confirmed by comparing with conventional H∞ control.

Adaptive self-tuning neurocontrol

A novel approach to adaptive direct neurocontrol is discussed in this paper. The objective is to construct an adaptive control scheme for unknown time-dependent nonlinear plants without using a model of the plant. The proposed approach is neural-network based and combines the self-tuning principle with reinforcement learning. The control scheme consists of a controller, a utility estimator, an exploration module, a learning module and a rewarding module. The controller and the utility estimator are implemented together in a single radial basis function network. The learning method involves structural adaptation (growing neural network) and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration–exploitation dilemma is solved through smooth transitions between the two modes. This enables rapid exploration response to novel plant dynamics and stable operation in the absence of changes in plant dynamics. The controller is capable of asymptotically approaching the desired reference trajectory, which is demonstrated in a simulation study.

Supervision and c-Means clustering of PID controllers for a solar power plant

A hierarchical control strategy consisting on a supervisory switching of PID controllers, simplified using the c-Means clustering technique, is developed and applied to the distributed collector field of a solar power plant. The main characteristic of this solar plant is that the primary energy source, the solar radiation, cannot be manipulated. It varies throughout the day, causing changes in plant dynamics conducting to distinct several operating points. To guarantee good performances in all operating points, a local PID controller is tuned to each operating point and a supervisory strategy is proposed and applied to switch among these controllers accordingly to the actual measured conditions. Each PID controller has been tuned off-line, by the combination of a dynamic recurrent non-linear neural network model with a pole placement control design. To reduce the number of local controllers, to be selected by the supervisor, a c-Means clustering technique was used. Simulation and experimental results, obtained at Plataforma Solar de Almería, Spain, are presented showing the effectiveness of the proposed approach.

ROBUST ADAPTIVE MODEL PREDICTIVE CONTROL OF A SOLAR PLANT WITH BOUNDED UNCERTAINTIES

This paper presents the application of a robust adaptive control scheme to a distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The controller uses a robust identification mechanism combined with a finite horizon receding controller to cope with the process dynamics having bounded uncertainties. The controller has been tested using a proven non-linear computer model of the field. Results obtained in the real plant are also shown. © 1997 John Wiley & Sons, Ltd.

Fuzzy logic control of a solar power plant

This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy inference systems, the TP (triangular partition) and TPE (triangular partition with evenly spaced midpoints) systems, is used to obtain adequate control signals in the whole range of possible operating conditions. The fuzzy logic controller has been tested in the real plant and results obtained are shown. A comparison with other control approaches widely used in the plant is performed using a nonlinear computer model of the field.

Distinction of Actuator Failures from Plant Dynamic Changes

Abstract This paper presents the theory of and design algorithms for a method detecting changes in the system transfer matrix (A) and distinguishing these from actuator failure/change in a dynamic system. This method utilizes optimal input to the failed/changed system to isolate hypothesized changes in A from other changes/failures. The change in the A matrix (i.e., ΔA) is related to hypothesized system failures or changes in operating point for a linearized nonlinear system. Once a failure is detected (i.e., via state error residuals from a state estimator), the input forces the system into the null space of A4 after which isolation is straightforward. This paper presents two theorems; one for the existence of solution for the optimal input and for the design freedom and the other for the actual solution in terms of feedback gain and user input freedom. In addition, design algorithms for the method are derived from the two thporems and from two additional lemmas. A specific example is given in which changes in A are detected and isolated from actuator failures for an advanced fighter aircraft.

Adaptive Robotic Deburring

The paper describes the development of an active end effector force control system for robotic deburring. The purpose of the control is to maintain a constant cutting force by making position adjustments normal to the part edge. Autoregressive moving-average (ARMA) models which relate the position command for the end effector to the resulting change in the cutting force were developed for the deburring process. A non-adaptive PID controller is designed and tested using real time control experiments. The results indicate a rise time of about 150 ms to a step change in the reference force. A force variation from the reference level of about 1 N peak-to-peak, which produced a chamfer variation of 0.12 mm peak-to-peak, was achieved. The robot speed used in these experiments was 25 mm/s.The application of parameter adaptive control is examined as a solution to the problem of deburring complex contours at higher robot speeds, and to improve the robustness of the control. Simulation results are presented for an adaptive version of the Smith predictor and a form of model reference adaptive control. The results suggest that the use of adaptive control could provide significant improvements in the speed of response and robustness to sudden changes in plant dynamics, over the non-adaptive controller.

対象特性変化時における手動制御動作のモデル

対象特性変化時における手動制御動作のモデル

Model of the Adaptive Behavior of the Human Operator in Response to a Sudden Change in the Control Situation

An adaptive model is presented to describe the behavior of the human operator in response to sudden changes in plant dynamics and transient disturbances. The plant simulated for tracking experiments is approximately second-order and has rate and attitude feedback augmentation for increased stability. The failure of the rate sensor and/or the attitude sensor results in a sudden transition in the order and gain of the effective plant dynamics. These failures may be accompanied by hard-over transient conditions in either the rate or attitude sensors. The adaptive model suggested has a variable structure, contains mode switching based on pattern recognition as evidence, and incorporates the decision-control logic required for successful adaptation to failures. The model in effect attempts to mimic the control strategy or algorithm used by a trained operator.

Decision Processes in the Adaptive Behavior of Human Controllers

A decision algorithm which simulates the rapid adaptive behavior of human controllers following sudden changes in plant dynamics is developed. The control of a VTOL aircraft in hover following failure of the stability augmentation system is used as a specific example. The decision algorithm is based on the assumption that the human controller recognizes certain pattern features in the error/error-rate phase plane. Experimental data, obtained from pilots facing four possible alternatives following the time of failure, are presented. The proposed decision algorithm is developed, and digital simulation results are discussed. A theoretical justification for the algorithm, based on statistical decision theory, is presented in the Appendix.