Rice planthoppers are long-distance migratory insects. The East Asian population is believed to migrate from northern Vietnam to southern China in the spring. To understand its major migration paths, a migration analysis was conducted with catch data by a single light trap located in the Red River delta in northern Vietnam. The catch data showed large peaks in late April to early May, each of which was used as a starting point of a simulation. Destination regions of simulated migrations were found to be distributed over southern Chinese provinces: Guangxi, southern Hunan, Jiangxi, northern Guangdong and northwestern Fujian. The region formed a diagonal belt stretching in the northeast direction. According to Chinese data, many planthoppers were caught in light traps along the diagonal belt region, supporting the simulation results. The planthoppers that arrive on rice plants of the early crop can multiply by one or two generations before their next emigration.
Iterative learning control (ILC) is a well-established approach for precision tracking control of systems, which perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework accompanied by a wide array of application studies, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) systems. Here, the presence of interacting dynamics often correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and establishes links between interaction dynamics and reduced robustness to modeling uncertainty, and slower convergence. It then shows how these and other limitations can be addressed by relaxing the tracking requirement to include only a subset of time points along the time duration. This is the first analysis to show how so-called 'point-to-point' ILC can address performance limitations associated with highly coupled systems. Theoretical observations are tested using a novel MIMO experimental test facility, which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results compare experimental observations with theoretical predictions over a wide range of interaction levels and with varying levels of injected noise.POINT-TO-POINT ITERATIVE LEARNING CONTROL 303Of the small number of practical studies that have been reported, interaction between dynamics has been assumed negligible and is generally not considered [1,19,20]. With mild interaction, one approach is to treat the coupling as an exogenous disturbance and design multiple SISO ILC loops. This has yielded satisfactory results when applied to control each joint of a six degree-offreedom industrial robot [21]. The approach has also been taken in stroke rehabilitation with ILC used to control the electrical stimulation applied to muscles in the upper limb [22]. However, in the foregoing cases, a robustness filter was required to prevent instability and the tracking accuracy was considerably larger than when controlling a single joint (with the remaining joints locked). In the case of more significant multivariable coupling, this approach may therefore be expected to lead to a further loss of tracking accuracy and the likelihood of instability. A similar approach is multiaxis inversion-based ILC, which assumes a square system matrix and employs a diagonal system inverse that contains only the dominant dynamics for each output [23]. Here, omission of non-dominant dynamics leads to lack of convergence at frequencies at which the omitted dynamics have an overly large norm.Other practical studies have employed MIMO ILC to tackle vibration suppression. For example, in [24], an ILC approach is applied to a six degree of freedom LCD substrate transfer robot to reduce end-effector vibration. The reference is modified off-line to reduce link vibration using redundant actuation degrees of freedom. Another MIMO vibration suppression approach is to develop ILC algorithms that have two separate...
Abstract-This paper examines the performance of a gradient-based point-to-point iterative learning control (ILC) algorithm applied to multivariable input, multivariable output (MIMO) systems. Whilst ILC is concerned with tracking a reference trajectory defined over a finite time duration, the point-to-point formulation addresses application domains where the output is not critical at all points over the task duration. The algorithm therefore enforces tracking of only an arbitrary subset of points, with the advantage that the convergence rate increases and input energy decreases as points are removed from the reference. Experimental results presented using a MIMO test facility which can be configured with variable levels of input-output interaction and exogenous disturbance/noise injection confirm the theoretical findings. I. INTRODUCTIONIterative Learning Control (ILC) is a methodology applicable to systems which repeatedly track a reference, y d (t), defined over a finite interval 0 ≤ t ≤ T . The aim is to use past experience to sequentially improve tracking performance over repeated trials of the task. It has been an area of intense research interest in both theoretical and application domains, see, for example, [1] for a recent literature review. However, rather than follow a motion profile defined at all points, in many applications the system output is only critical at a finite set of prescribed time instants. Examples include production line automation, crane control, satellite positioning, and robotic 'pick and place' tasks in which the critical points correspond to the location of the payloads.The standard ILC framework is able to tackle the point-topoint problem simply by employing an arbitrary reference, y d (t), which passes through the desired points. However superior results follow if this is coupled with strategies such as Input Shaping in order to suppress vibrations that occur between the critical points. This approach is taken in [2] for a high-acceleration positioning table. An alternative is to use a simpler feedback controller to track y d (t) and to employ ILC to update parameters within the input shaping filter applied to the reference, as proposed by [3] for control of an industrial robot. Another approach is to develop ILC algorithms which have two separate components; one which ensures tracking of y d (t), and another which reduces the amplitude of residual vibrations occurring after the point-topoint location is reached [4].The drawback to all these methods is that they fail to utilize the extra freedom available in ILC design to satisfy
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