State estimation for fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays

This paper deals with the problem of state estimation for fuzzy cellular neural networks (FCNNs) with time delay in the leakage term, discrete and unbounded distributed delays. In this paper, leakage delay in the leakage term is used to unstable the neuron states. It is challenging to develop a delay dependent condition to estimate the unstable neuron states through available output measurements such that the error-state system is globally asymptotically stable. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term, an improved delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). However, by using the free-weighting matrices method, a simple and efficient criterion is derived in terms of LMIs for estimation. The restriction such as the time-varying delay which was required to be differentiable or even its time-derivative which was assumed to be smaller than one, are removed. Instead, the time-varying delay is only assumed to be bounded. Finally, numerical examples and its simulations are given to demonstrate the effectiveness of the derived results.     

Amortized constant time state estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter

The computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recovering the state mean and covariance requires the inversion of a matrix with the size of the state, which is computationally too expensive in time and memory for large problems. Exactly sparse state representations, such as that of Pose SLAM, alleviate the cost of state recovery either in time or in memory, but not in both. In this paper, we present an approach to state estimation that is linear both in execution time and in memory footprint at loop closure, and constant otherwise. The method relies on a state representation that combines the Kalman and the information-based approaches. The strategy is valid for any SLAM system that maintains constraints between marginal states at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.     

Electrocatalytic reduction of hydrogen peroxide and its determination in antiseptic and soft-glass cleaning solutions at phosphotungstate-doped-glutaraldehyde-cross-linked poly-l-lysine film electrodes

The present work describes the electrocatalytic behavior of phosphotungstate-doped glutaraldehyde-cross-linked poly-l-lysine (PLL-GA-PW) film electrode towards reduction of hydrogen peroxide (H₂O₂) in acidic medium. The modified electrode was prepared by means of electrostatically trapping the phosphotungstate anion into the cationic PLL-GA coating on glassy carbon electrode. The PLL-GA-PW film electrode showed excellent electrocatalytic activity towards H₂O₂ reduction in 0.1 M H₂SO₄. Under the optimized conditions, the electrochemical sensor exhibited a linear response for H₂O₂ concentration over the range 2.5 × 10⁻⁶ to 6.85 × 10⁻³ M with a sensitivity of 1.69 μA mM⁻¹. The curvature in the calibration curve at high concentration is explained in terms of Michaelis-Menten (MM) saturation kinetics, and the kinetics parameters calculated by three different methods were compared. The PLL-GA-PW film electrode did not respond to potential interferents such as dopamine, ascorbic acid and uric acid. This unique feature of PLL-GA-PW film electrode allowed selective determination of H₂O₂. Finally, the proposed electrochemical sensor was successfully applied to determine H₂O₂ in commercially available antiseptic solution and soft-contact lenses cleaning solution and the method has been validated using independent estimation by classical potassium permanganate titration method. Major advantages of the method are simple electrode fabrication, stability and high selectivity towards hydrogen peroxide.     

Copula and s-map on a quantum logic

A quantum logic is one of possible mathematical models for non-compatible random events. In this paper, we introduce and study functions called QL-copula and QL-co-copula on a quantum logic and we compare them with the classical definition of copula and co-copula, respectively. Finally, we show several examples of these functions by means of an s-map.     

Application of the OBD method for optimization of neural state variable estimators of the two-mass drive system

This paper presents neural estimators of the mechanical state variables of the electrical drive system with elastic joints. The non-measurable state variables, as the torsional torque and the load machine speed are estimated using multilayer feed-forward neural networks. The main stages of the design methodology of these neural estimators are presented. The optimal brain damage method is implemented for the structure optimization of each neural network. Then signals estimated by neural estimators are tested in the electrical drive control structure with additional feedbacks from the estimated shaft torque and the difference between the motor and the load speeds. The simulation results show good accuracy of both presented neural estimators for the wide range of changes of the reference speed and the load torque. The simulation results are then verified by laboratory experiments.     

输入饱和及带宽限制下高超飞行器的闭环稳定边界研究

针对于吸气式高超声速飞行器开环不稳定的动力学特性,研究了控制信号存在饱和约束及带宽限制条件下的闭环稳定边界.首先,简要介绍了吸气式高超声速飞行器的建模方法与动力学特性的主要问题.考虑到飞行器控制信号的幅值限制及带宽约束,综合高超声速飞行器的开环不稳定特性,定量地分析了系统的闭环稳定边界:与系统不稳定极点的位置,其对应的左特征向量及控制信号的幅值约束有关;执行器的带宽限制在此基础上进一步缩小了反馈控制系统的稳定边界.根据高超声速飞行器短周期不稳定特性,解析地给出了闭环稳定边界的计算公式.采用蒙特卡洛分析方法对闭环系统的稳定边界及滑模变结构控制器作用下的稳定区域进行验证.仿真结果与理论分析具有一致性,验证了系统开环特性对于闭环稳定性的限制及控制信号带宽约束对稳定性的影响.     

State transformation-based dynamic visual servoing for an unmanned aerial vehicle

In this paper, we propose a visual servoing control for a quadrotor unmanned aerial vehicle (UAV) which is based on a state transformation technique. The UAV is equipped with a single downwards facing camera, and the motion control objective is the regulation of relative displacement and yaw to a stationary visual target located on the ground. The state transformation is defined by a system of partial differential equations (PDEs) which eliminate roll and pitch rate dependence in the transformed image feature kinematics. A method for computing the general solutions of these PDEs is given, and we show a particular solution reduces to an established virtual camera approach. We treat point and line cases and introduce image moment features defined in the virtual camera image plane. Robustness of the control design is improved by accounting for attitude measurement bias, and uncertainty in thrust gain, mass, and image feature depth. The asymptotic stability of the closed-loop is proven. The method is based on a simple proportional-integral-derivative (PID) structure which can be readily implemented on-board. Experimental results show improved performance relative to previous work.     

Robust stabilisation and L2 -gain analysis for switched systems with actuator saturation under asynchronous switching

Robust stabilisation and L₂-gain analysis for a class of switched systems with actuator saturation are studied in this paper. The switching signal of the controllers lags behind that of the system modes, which leads to the asynchronous switching between the candidate controllers and the subsystems. By combining the piecewise Lyapunov function method with the convex hull technique, sufficient conditions in terms of LMIs for the solvability of the robust stabilisation and weighted L₂-gain problems are presented respectively under the dwell time scheme. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed results.     

Co-design of H∞ jump observers for event-based measurements over networks

This work presents a strategy to minimise the network usage and the energy consumption of wireless battery-powered sensors in the observer problem over networks. The sensor nodes implement a periodic send-on-delta approach, sending new measurements when a measure deviates considerably from the previous sent one. The estimator node implements a jump observer whose gains are computed offline and depend on the combination of available new measurements. We bound the estimator performance as a function of the sending policies and then state the design procedure of the observer under fixed sending thresholds as a semidefinite programming problem. We address this problem first in a deterministic way and, to reduce conservativeness, in a stochastic one after obtaining bounds on the probabilities of having new measurements and applying robust optimisation problem over the possible probabilities using sum of squares decomposition. We relate the network usage with the sending thresholds and propose an iterative procedure for the design of those thresholds, minimising the network usage while guaranteeing a prescribed estimation performance. Simulation results and experimental analysis show the validity of the proposal and the reduction of network resources that can be achieved with the stochastic approach.