Eleven organically grown apple cultivars and 11 apple cultivars of integrated production from Austria and Slovenia were analyzed by HPLC for the content of phenolic compounds in peel and pulp. We identified chlorogenic acid, p-coumaric acid, procyanidin B3, protocatechuic acid, (−)-epicatechin, phloridzin, rutin and quercetin-3-rhamnoside in apple peel. In apple pulp, (+)-catechin was also identified in all the cultivars. Some other phenols (procyanidin B3, rutin and quercetin-3-rhamnoside) could not be identified or were not properly separated. With regard to the phenolic content in the apple peel, there were no differences between organically grown apple cultivars and apple cultivars of integrated production. Organically grown apples, however, exhibited a higher content of phenolic substances in the apple pulp compared with the apple cultivars of integrated production. This may be due either to the different genotype source or to the growing technology. Higher concentrations of phenolic compounds in organically grown cultivars could be a result of plant response to stress. The apple peel contained higher concentrations of identified phenols than the pulp. The apple peel represents up to 10% of the whole fruit; therefore the phenolsic compounds in the pulp are of greater importance to the consumer than the phenolic compounds in the peel.
An approximate model predictive control approach is applied on an unmanned aerial vehicle with limited computational resources. A novel method using a continuous time parametrization of the state and input trajectory is used to derive a compact description of the optimal control problem. Different first order methods for the online optimization are discussed in terms of memory requirements and execution time. The generalized fast dual gradient method is implemented on the aerial vehicle. The approximate model predictive control algorithm runs on an embedded platform with a STM32 Cortex M4 processor. Simulation studies show that the model predictive controller outperforms a linear quadratic regulator in aggressive maneuvers. The model predictive control approach is evaluated in practice and shown to yield satisfactory flight behavior.
In this paper we present the design of a hybrid robotic arm using soft, inflatable bladders for actuation. Low cost switching valves are used for pressure control, where the valve model is identified experimentally. A model of the robotic arm is derived based on system identification and used to derive a linear quadratic Gaussian controller. A method to solve limitations of the employed switching valves is proposed and experimentally proven to improve tracking performance. The closed loop control performance of the robotic arm is demonstrated by stabilizing a rotational inverted pendulum known as the Furuta pendulum.
This paper presents the application of an iterative learning control scheme to improve the position tracking performance for an articulated soft robotic arm during aggressive maneuvers. Two antagonistically arranged, inflatable bellows actuate the robotic arm and provide high compliance while enabling fast actuation. Switching valves are used for pressure control of the soft actuators. A norm-optimal iterative learning control scheme based on a linear model of the system is presented and applied in parallel with a feedback controller. The learning scheme is experimentally evaluated on an aggressive trajectory involving set point shifts of 60 degrees within 0.2 seconds. The effectiveness of the learning approach is demonstrated by a reduction of the root-mean-square tracking error from 13 degrees to less than 2 degrees after applying the learning scheme for less than 30 iterations.
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