Adaptive robust approximate constraint-following control for mechanical systems

We formulate control problems of mechanical systems as constraint following. The system contains uncertainty which is (possibly fast) time-varying. It is unknown but bounded. The bound is, however, unknown. The objective is to design control which renders approximate constraint following. Adaptive laws are constructed. The controls are then based on the adaptive parameters. We are able to demonstrate guaranteed system performance, regardless of the uncertainty.     

A delay-derivative-dependent approach to robust H∞ filter design for uncertain systems with time-varying distributed delays

This paper focuses on the problem of robust H∞ filter design for uncertain systems with time-varying state and distributed delays. System uncertainties are considered as norm-bounded time-varying parametric uncertainties. The delays are assumed to be time-varying delays being differentiable uniformly bounded with delay-derivative bounded by a constant, which may be greater than one. A new delay-derivative-dependent approach of filter design for the systems is proposed. A novel Lyapunov-Krasovskii functional (LKF) is employed, and a tighter upper bound of its derivative is obtained by employing an inequality and using free-weighting matrices technique, then the proposed result has advantages over some existing results, in that it has less conservatism and it enlarges the application scope. An improved sufficient condition for the existence of such a filter is established in terms of linear matrix inequality (LMI). Finally, illustrative examples are given to show the effectiveness and reduced conservatism of the proposed method.     

Switching position and range-domain carrier-smoothing-code filtering for GNSS positioning in harsh environments with intermittent satellite deficiencies

Carrier-smoothing-code filtering (CSCF) is widely used in GNSS signal processing to combine code pseudoranges and carrier phases. Position-domain (PD) CSCF is generally more accurate and less sensitive to visible satellite changes than range-domain (RD) CSCF. However, PD-CSCF necessitates at least four visible satellites. Intermittent satellite deficiency with less than four visible satellites is not uncommon in harsh environments like urban canyons. At such deficiency epochs, the PD-CSCF convergence has to break off. This study aims to bridge intermittent deficiency epochs in PD-CSCF without introducing any external information. The proposed solution is called switching RD and PD CSCF in which PD filter is replaced by RD filter at deficiency epochs. Besides detailing the seamless switching algorithms from PD/RD to RD/PD filters, a global RD filter, different from the conventional one, is developed to preserve the correlations among smoothed pseudoranges corresponding to different satellites. Compared to the conventional PD and RD CSCF algorithms, smoother results can be expected from the proposed switching filter, especially after the intermittent deficiency epochs. Experiments are conducted using real BDS signals. Cases with different kinds of deficiency are considered. Superiority of the proposed method is clearly observed from the results.     

FCA based ontology development for data integration

Data is a valuable asset to our society. Effective use of data can enhance productivity of business and create economic benefit to customers. However with data growing at unprecedented rates, organisations are struggling to take full advantage of available data. One main reason for this is that data is usually originated from disparate sources. This can result in data heterogeneity, and prevent data from being digested easily. Among other techniques developed, ontology based approaches is one promising method for overcoming heterogeneity and improving data interoperability. This paper contributes a formal and semi-automated approach for ontology development based on Formal Concept Analysis (FCA), with the aim to integrate data that exhibits implicit and ambiguous information. A case study has been carried out on several non-trivial industrial datasets, and our experimental results demonstrate that proposed method offers an effective mechanism that enables organisations to interrogate and curate heterogeneous data, and to create the knowledge that meets the need of business.     

Switching topology approach for UAV formation based on binary-tree network

Formation control is one of the most active topics within the realm of coordination fields for unmanned aerial vehicles (UAVs). The formation pattern is an essential aspect which mainly influences the status of formation flight. This paper focuses on the switching topology approach for a few specific quadrotor UAV formations. A novel switching method based on the binary-tree network (BTN) is developed to realize the transformations between the V-shape and the complete binary tree shape (CBT-shape) topologies. The typical feature of the BTN is the chain structure. Based on the cascaded form of the BTN, a low level feedback controller is designed for the guidance and coordination of UAV swarms. Simulation results demonstrate that the BTN based approach is more superior in the computational cost and real-time performances than several traditional methods.     

Coupling and disturbance characterization based robust control for manned submersibles

A novel coupling and disturbance characterization based control (CDC-BC) scheme is investigated for manned submersibles with strong couplings and disturbances. Firstly, the coupling characterization index (CCI) and disturbance characterization index (DCI) are defined to indicate whether the couplings and disturbances harm or benefit the manned submersible system. Then, the coupling and disturbance characterization based control (CDC-BC) method is developed to eliminate the detrimental couplings and disturbances, as well as to retain the beneficial ones. Moreover, it is also proved that the tracking errors of the closed-loop system will converge to zero asymptotically. Finally, some simulation results demonstrate the effectiveness of the proposed control scheme.     

Bias-compensated affine-projection-like algorithm based on maximum correntropy criterion for robust filtering

This article proposes an affine-projection-like maximum correntropy (APLMC) algorithm for robust adaptive filtering. The proposed APLMC algorithm is derived by using the objective function based on the maximum correntropy criterion (MCC), which can availably suppress the bad effects of impulsive noise on filter weight updates. But the overall performance of the APLMC algorithm may be decreased when the input signal is polluted by noise. To compensate for the deviation of the APLMC algorithm in the input noise interference environment, the bias compensation (BC) method is introduced. Therefore, the bias-compensated APLMC (BC-APLMC) algorithm is presented. Besides, the convergence of the BC-APLMC algorithm in the mean and the mean square sense is studied, which provides a constraint range for the step-size. Computer simulation results show that the APLMC, and BC-APLMC algorithms are valid in acoustic echo cancellation and system identification applications. It also shows that the proposed algorithms are robust in the presence of input noise and impulse noise.     

基于抗差算法的马斯京根参数估计

用于估计马斯京根模型参数的方法很多,但这些方法在数据存在异常值时缺乏抵御异常值影响的抗差性能. 推导出一种有限制条件的参数抗差估计算法,通过含有随机误差和异常误差的人工数据和真实数据比较抗差算法与传统最小二乘算法的抗差性. 研究表明抗差估计算法能减小异常值对参数估值的影响.     

基于数据量度模型的信息系统整合效益评价

信息系统整合的关键是数据整合。传统的基于业务的信息系统效益评价方式用于评价数据整合的效益略显不足。本文提出基于数据量度模型的系统整合效益评价体系,并介绍其应用案例。     

An event-triggered based robust control for electro-hydraulic servo machines with active disturbances rejection

From an interdisciplinary perspective, the event-triggered scheme, the state observer, and the nonlinear disturbance observer are introduced in a robust tracking control to study the networked electro-hydraulic servo system control problems with digital communication challenge, sensor installation restricted problem, matched modeling uncertainties, and mismatched disturbances. Control packets are likely to be delayed or even lost in the networked control system when the communication medium is shared by multiple nodes and the available communication bandwidth is limited. Therefore, it is necessary to save communication resources. To improve the control performance and the efficiency of the network resource utilization, the event-triggered scheme is introduced. Specifically, the practical application of the event-triggered scheme in an actual electro-hydraulic servo system is a breakthrough in this paper. In addition, to obtain the real-time states of the unmeasurable system and compensate for both matched disturbances and external disturbances simultaneously, the state observer and the nonlinear disturbance observer are collaboratively designed. Finally, to evaluate the control performance of the designed controller, the related comparative experiments are carried out in an actual system. The results show that theoretical analysis and experimentation are cross verified.     

Energy management strategy for parallel hybrid electric vehicles based on approximate dynamic programming and velocity forecast

In parallel hybrid electric vehicles (HEVs), the power split between the engine and the electric motor as well as the gear shift in the gearbox determines the overall energy efficiency. In this paper an adaptive energy management strategy with velocity forecast is proposed to optimize the fuel consumption in parallel HEVs, which is formulated into a mixed-integer optimization problem. Approximate dynamic programming with a novel actor-gear-critic design is presented for simultaneously controlling the power split and gear shift online. The power split as a continuous variable is determined from an actor network to realize the energy distribution between two power sources. The gear shift as a discrete variable is obtained from a gear network to adjust the gear ratio in the gearbox. The concept enables an online learning of the energy management strategy for different driving behaviors without the requirement of a system model and the driving cycle. Simulation results indicate that the proposed strategy achieves close fuel economy compared with the optimal solutions resulting from dynamic programming. Furthermore, a multi-stage neural network is introduced for velocity forecast, providing a computationally efficient training framework with good prediction performance. The velocity prediction is finally combined with the energy management strategy for an effective application and fuel economy.     

Bond graph based sensitivity and uncertainty analysis modelling for micro-scale multiphysics robust engineering design

Components within micro-scale engineering systems are often at the limits of commercial miniaturization and this can cause unexpected behavior and variation in performance. As such, modelling and analysis of system robustness plays an important role in product development. Here, schematic bond graphs are used as a front end in a sensitivity analysis based strategy for modelling robustness in multi-physics micro-scale engineering systems. As an example, the analysis is applied to a behind-the-ear (BTE) hearing aid.By using bond graphs to model power flow through components within different physical domains of the hearing aid, a set of differential equations to describe the system dynamics is collated. Based on these equations, sensitivity analysis calculations are used to approximately model the nature and the sources of output uncertainty during system operation. These calculations represent a robustness evaluation of the current hearing aid design and offer a means of identifying potential for improved designs of multiphysics systems by way of key parameter identification.     

Adaptive robust control for planar n-link underactuated manipulator based on radial basis function neural network and online iterative correction method

This paper presents an adaptive robust control strategy based on a radial basis function neural network (RBFNN) and an online iterative correction method (OICM) for a planar n-link underactuated manipulator with a passive first joint to realize its position control objective. An uncertain model of the planar n-link underactuated manipulator is built, which contains the parameter perturbation and the external disturbance. The adaptive robust controllers based on the RBFNN are designed to realize the model reduction, which makes the system reduce to a planar virtual three-link underactuated manipulator (PVTUM) and simplifies the complexity of the system control. An online differential evolution (DE) algorithm is used to calculate the target angles of the PVTUM based on the nominal model parameters. The control of the PVTUM is divided into two stages, and the adaptive robust controllers are still employed to realize the control objective of each stage. Then, the OICM is used to correct the deviations of all link angles of the PVTUM caused by the parameter perturbation, which makes the end-point of the system gradually approach to its target position. Finally, simulation results of a planar four-link underactuated manipulator demonstrate the effectiveness of the proposed adaptive robust control strategy.     

Gradient iterative algorithm for dual-rate nonlinear systems based on a novel particle filter

This paper proposes a novel particle filter based gradient iterative algorithm for the identification of dual-rate nonlinear systems. The novel particle filter is applied to estimate the missing outputs, and the measurable outputs are utilized to adjust the weights of particles during each interval of the slow sampled rate. Then the missing outputs and the unknown parameters can be estimated iteratively by the novel particle filter based gradient iterative algorithm. The simulation results indicate that the proposed method is more effective than the classical auxiliary model method.     

Correntropy based model predictive controller with multi-constraints for robust path trajectory tracking of self-driving vehicle

Self-driving vehicles must be equipped with path tracking capability to enable automatic and accurate identification of the reference path. Model Predictive Controller (MPC) is an optimal control method that has received considerable attention for path tracking, attributed to its ability to handle control problems with multiple constraints. However, if the data acquired for determining the reference path is contaminated by non-Gaussian noise and outliers, the tracking performance of MPC would degrades significantly. To this end, Correntropy-based MPC (CMPC) is proposed in this paper to address the issue. Different from the conventional MPC model, the objective of CMPC is constructed using the robust metric Maximum Correntropy Criterion (MCC) to transform the optimization problem of MPC to a non-concave problem with multiple constraints, which is then solved by the Block Coordinate Update (BCU) framework. To find the solution efficiently, the linear inequality constraints of CMPC are relaxed as a penalty term. Furthermore, an iterative algorithm based on Fenchel Conjugate (FC) and the BCU framework is proposed to solve the relaxed optimization problem. It is shown that both objective sequential convergence and iterate sequence convergence are satisfied by the proposed algorithm. Simulation results generated by CarSim show that the proposed CMPC has better performance than conventional MPC in path tracking when noise and outliers exist.     

Non-fragile robust H∞ control for uncertain discrete-time singular systems with time-varying delays

In this paper, the problem of delay-dependent non-fragile robust H∞$H\infty$ control for a class of discrete-time singular systems with state-delay and parameter uncertainties is investigated. Based on singular value decomposition approach, a delay-dependent sufficient condition for the H∞$H\infty$ control problem for a class of discrete-time singular systems is proposed by constructing generalized Lyapunov–Krasovskii function and a new difference inequality. A memoryless state feedback controller under controller gain perturbations is designed, which guarantees that, for all admissible uncertainties, the resultant closed-loop system is regular, causal, and stable with an H∞$H\infty$ norm bound constraint. Numerical examples in the last will show that our results have the better performance in conservativeness than some results reported in the literature.     

Dynamical robust H∞ filtering for nonlinear uncertain systems: An LMI approach

In this paper, a new approach to robust H_∞ filtering for a class of nonlinear systems with time-varying uncertainties is proposed in the LMI framework based on a general dynamical observer structure. The nonlinearities under consideration are assumed to satisfy local Lipschitz conditions and appear in both state and measured output equations. The admissible Lipschitz constants of the nonlinear functions are maximized through LMI optimization. The resulting H_∞ observer guarantees asymptotic stability of the estimation error dynamics with prespecified disturbance attenuation level and is robust against time-varying parametric uncertainties as well as Lipschitz nonlinear additive uncertainty.     

Mixed complementarity problems for robust optimization equilibrium under l1∩l∞-norm

We consider robust optimization equilibrium for the bimatrix game based on robust optimization method. In this paper, each player may neither exactly estimate his opponent's strategies nor evaluate his own cost matrix accurately while may estimate a bounded uncertain set. We obtain that the robust optimization equilibrium problem can be formulated as a mixed complementarity problem (MCP) under _l1∩_l∞-norm$l_1\cap l_{\infty }\mathrm{-}\mathrm{norm}$. Some numerical examples are presented to illustrate the behavior of robust optimization equilibrium.     

Non-fragile H∞ filter design with sparse structure for linear discrete-time systems

This paper presents a study on the problem of designing non-fragile _H∞$H_{\infty }$ filters with sparse structure for linear discrete-time systems. The filters to be designed with sparse structure are assumed to be with additive gain variations, which are resulted from filter implementations. Firstly, a class of sparse structures is specified from a given fully parameterized _H∞$H_{\infty }$ filter. Then, an LMI-based procedure for designing non-fragile _H∞$H_{\infty }$ filters with the sparse structure is provided. The resulting design guarantees the augmented system asymptotically stable and the _H∞$H_{\infty }$ attenuation level less than a prescribed level. A numerical example is given to illustrate the proposed method.