Robust model reference control for second-order descriptor linear systems subject to parameter uncertainties

This paper is devoted to designing the robust model reference controller for uncertain second-order descriptor linear systems subject to parameter uncertainties. The parameter uncertainties are assumed to be norm-bounded. The design of a robust controller can be divided into two separate problems: a robust stabilization problem and a robust compensation problem. Based on the solution of generalized Sylvester matrix equations, we obtain some sufficient conditions to guarantee the complete parameterization of the robust controller. The parametric forms are expressed by a group of parameter vectors which reveal the degrees of freedom existing in the design of the compensator and can be utilized to solve the robust compensation problem. In order to reduce the effect of parameter uncertainties on the tracking error vector, the robust compensation problem is converted into a convex optimization problem with a set of linear matrix equation constraints. A simulation example is provided to illustrate the effectiveness of the proposed technique.

An Integrated Compensation Method for the Force Disturbance of a Six-Axis Force Sensor in Complex Manufacturing Scenarios

As one of the key components for active compliance control and human–robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy.

Human-Wildlife Conflict and its Implication for Conservation at Sundarpur, Udayapur, Eastern Nepal

Local people are the major stakeholders of biodiversity conservation. Human-wildlife conflict (HWC) could result in a negative attitude of the general public towards wildlife adding challenges for conservation. This is more applicable in the landscapes which are outside the protected area (PA) coverage. But, the majority of HWC related studies in Nepal have centered on PAs and their peripheries. This study documents the prevailing situation of HWC in Sundarpur of Udayapur district that shelters some HWC prone wildlife species, while situating outside PA. Data about conflict and people's perception of wildlife conservation was collected using household surveys supplemented by key informant interviews and direct observation. Monkeys (93%, n=93) and elephants (86%, n=86) were found to be the major animals involved in the conflict, mostly resulting in crop raiding, the major form of conflict as reported by (95%, n=95) of respondents. Livestock depredation cases were mostly by common leopard (84%, n=21) and sloth bear was involved in the majority of human attack cases (90%, n=9). The results showed increasing trend of conflicts for elephants (63%, n=63) and monkeys (73%, n=73), while declining trend for sloth bear (64%, n=64), wild boar (85%, n=85), and leopard (46%, n=46). People believed the natural attraction of wildlife towards crops and livestock to be the major driving factor of conflict. Majority of respondents had a positive attitude towards wildlife conservation. However, implementation of community based conflict management strategies, robust compensation schemes along with conservation education programs are highly essential to achieve desired conservation success.

Structural and functional synaptic plasticity induced by convergent synapse loss requires co-innervation in the Drosophila neuromuscular circuit

ABSTRACTThroughout the nervous system, the convergence of two or more presynaptic inputs on a target cell is commonly observed. The question we ask here is to what extent converging inputs influence each other’s structural and functional synaptic plasticity. In complex circuits, isolating individual inputs is difficult because postsynaptic cells can receive thousands of inputs. An ideal model to address this question is the Drosophila larval neuromuscular junction where each postsynaptic muscle cell receives inputs from two glutamatergic types of motor neurons (MNs), known as 1b and 1s MNs. Notably, each muscle is unique and receives input from a different combination of 1b and 1s motor neurons. We surveyed synapses on multiple muscles for this reason. Here, we identified a cell-specific promoter to ablate 1s MNs after innervation. Additionally, we genetically blocked 1s innervation. Then we measured 1b MN structural and functional responses using electrophysiology and microscopy. For all muscles, 1s MN ablation resulted in 1b MN synaptic expansion and increased basal neurotransmitter release. This demonstrates that 1b MNs can compensate for the loss of convergent inputs. However, only a subset of 1b MNs showed compensatory evoked activity, suggesting spontaneous and evoked plasticity are independently regulated. Finally, we used DIP-α mutants that block 1s MN synaptic contacts; this eliminated robust 1b synaptic plasticity, raising the possibility that muscle co-innervation may define an activity “set point” that is referenced when subsequent synaptic perturbations occur. This model can be tested in more complex circuits to determine if co-innervation is fundamental for input-specific plasticity.SIGNIFICANCE STATEMENTIn complex neural circuits, multiple converging inputs contribute to the output of each target cell. Thus, each input must be regulated, but whether adjacent inputs contribute to this regulation is unclear. To examine input-specific synaptic plasticity in a structurally and functionally tractable system, we turn to the Drosophila neuromuscular circuit. Each muscle is innervated by a unique pair of motor neurons. Removal of one neuron after innervation causes the adjacent neuron to increase synaptic outgrowth and functional output. However, this is not a general feature since each MN differentially compensates. Also, robust compensation requires co-innervation by both neurons. Understanding how neurons respond to perturbations in adjacent neurons will provide insight into nervous system plasticity in both healthy and diseased states.

Robust model reference control for uncertain second-order system subject to parameter uncertainties

This paper is devoted to designing a robust model reference controller for uncertain second-order systems subject to parameter uncertainties. The system matrix of the first-order reference model is more general and the parameter uncertainties are assumed to be norm-bounded. The design of robust controller can be devided into two separate problems: problem robust stabilization and problem robust compensation. Based on the solution of generalized Sylvester matrix equations, we obtain some sufficient conditions to guarantee the complete parameterization of the controller. Then, the problem robust compensation of the closed-loop system is estimated by solving a convex optimisation problem with a set of linear matrix equations constraints. Two simulation examples are provided to illustrate the effectiveness of the proposed technique.