Pieter Janssens

A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI Systems

This brief presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-to-point motion problems. The key contribution of this brief is the estimation of the systems impulse response using input/output measurements from previous iterations, hereby eliminating time-consuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to: 1) achieve tracking accuracy up to the repeatability of the test setup; 2) minimize the rms value of the tracking error while respecting the actuator input constraints; 3) learn energy-optimal system inputs for point-to-point motions.

Initialization of ILC based on a previously learned trajectory

Iterative learning control (ILC) is an open-loop control strategy that learns the system input to track a desired trajectory from previous executions. A major limitation of ILC is that for every new trajectory, the ILC is reinitiated and thus takes a number of iterations to learn the new optimal system input. This paper presents a novel methodology for linear time-invariant systems to calculate a better initialization of an ILC based on a previously learned similar trajectory and a disturbance model. To illustrate the potential of the developed method, it is applied to a permanent magnet linear motor and compared to a model-based feedforward control scheme. The experimental results show that the proposed method outperforms the model-based feedforward control scheme in the case of similar motion trajectories, yielding a better initialization of an ILC.

Model-free iterative learning control for LTI systems and experimental validation on a linear motor test setup

This paper presents a novel model-free iterative learning control algorithm for linear time-invariant systems with actuator constraints. At every trial, a finite impulse response filter to update the system input is computed by solving a convex optimization problem that minimizes the next trials tracking error while accounting for actuator constraints. The presented iterative learning control algorithm is validated on a linear motor positioning system. Experimental results show the ability of the proposed model-free algorithm to learn the optimal system input in the presence of cogging forces and actuator input constraints.

The effect of drought stress on heterozygosity-fitness correlations in pedunculate oak (Quercus robur).

BACKGROUND AND AIMS The interaction between forest fragmentation and predicted climate change may pose a serious threat to tree populations. In small and spatially isolated forest fragments, increased homozygosity may directly affect individual tree fitness through the expression of deleterious alleles. Climate change-induced drought stress may exacerbate these detrimental genetic consequences of forest fragmentation, as the fitness response to low levels of individual heterozygosity is generally thought to be stronger under environmental stress than under optimal conditions. METHODS To test this hypothesis, a greenhouse experiment was performed in which various transpiration and growth traits of 6-month-old seedlings of Quercus robur differing in multilocus heterozygosity (MLH) were recorded for 3 months under a well-watered and a drought stress treatment. Heterozygosity-fitness correlations (HFC) were examined by correlating the recorded traits of individual seedlings to their MLH and by studying their response to drought stress. KEY RESULTS Weak, but significant, effects of MLH on several fitness traits were obtained, which were stronger for transpiration variables than for the recorded growth traits. High atmospheric stress (measured as vapour pressure deficit) influenced the strength of the HFCs of the transpiration variables, whereas only a limited effect of the irrigation treatment on the HFCs was observed. CONCLUSIONS Under ongoing climate change, increased atmospheric stress in the future may strengthen the negative fitness responses of trees to low MLH. This indicates the necessity to maximize individual multilocus heterozygosity in forest tree breeding programmes.

Model-free iterative learning of time-optimal point-to-point motions for LTI systems

This paper presents a model-free iterative learning control algorithm, which generates time-optimal point-to-point motions for linear time-invariant systems. The proposed optimization-based algorithm consists of two levels. At the first level, a bisection algorithm determines the fastest possible point-to-point motion, i.e. the motion time is minimized, subject to actuator limitations. At the second level, an iterative learning control algorithm for point-to-point motions learns the system input that results in a point-to-point motion, with the minimal motion time obtained by the bisection algorithm at the first level. Simulation results show that the proposed model-free method is able to learn the time-optimal system input for a given point-to-point motion problem in the presence of measurement noise and repeating disturbances.

Iterative learning control for optimal path following problems

In optimal path following problems the motion along a given geometric path is optimized according to a desired objective while accounting for the system dynamics and system constraints. In the case of time-optimal path following, for example, the system input to move along the geometric path in minimal time is computed. In practice however, due to model-plant mismatch, (i) the geometric path is not followed exactly, and (ii) the optimized trajectory might be suboptimal, or even infeasible for the true plant. Assuming that the system performs the task repeatedly, this paper proposes an iterative learning control approach to improve the path following performance. The proposed learning algorithm is experimentally validated for a time-optimal path following problem on an XY-table. The results show that the developed ILC approach improves both the execution time and the accuracy significantly.

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

Abstract: Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements ( i.e. , hyperspectral canopy reflectance measurements) as well as yield determination ( i.e. , total yield and number of fruits per tree) and quality assessment ( i.e ., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of

Model-free iterative learning control for LTI systems with actuator constraints

Abstract This paper presents a model-free iterative learning control algorithm for linear time-invariant systems. At every trial, a finite impulse response filter to update the system input is calculated by solving a convex optimization problem that minimizes the next trials tracking error taking into account actuator constraints. Simulation results show the ability of the proposed model-free method to deal with actuator constraints and to fully compensate for trial-invariant disturbances such as actuator cogging.

Energy-Optimal Time Allocation of a Series of Point-to-Point Motions

When a system has to perform a series of point-to-point (PTP) motions within a given time, the execution time for each individual motion is, in many cases, free to choose. This letter presents a dynamic programming algorithm to allocate the time to each motion in a series of PTP motions in an energy-optimal way. The algorithm is demonstrated on an xy-positioning stage and the results are compared with an equal time allocation scheme.

Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

Stress-related biophysical variables of capital intensive orchard crops can be estimated with proxies via spectral vegetation indices from off-nadir viewing satellite imagery. However, variable viewing compositions affect the relationship between spectral vegetation indices and stress-related variables (i.e., chlorophyll content, water content and Leaf Area Index (LAI)) and could obstruct change detection. A sensitivity analysis was performed on the estimation of biophysical variables via vegetation indices for a wide range of viewing geometries. Subsequently, off-nadir viewing satellite imagery of an experimental orchard was analyzed, while all influences of background admixture were minimized through vegetation index normalization. Results indicated significant differences between nadir and off-nadir viewing scenes (∆R2 > 0.4). The Photochemical Reflectance Index (PRI), Normalized Difference Infrared Index (NDII) and Simple Ratio Pigment Index (SRPI) showed increased R2 values for off-nadir scenes taken perpendicular compared to parallel to row orientation. Other indices, such as Normalized Difference Vegetation Index (NDVI), Gitelson and Merzlyak (GM) and Structure Insensitive Pigment Index (SIPI), showed a significant decrease in R2 values from nadir to off-nadir viewing scenes. These results show the necessity of vegetation index selection for variable viewing applications to obtain an optimal derivation of biophysical variables in all circumstances.