An Intelligent Non-Collocated Control Strategy for Ball-Screw Feed Drives with Dynamic Variations

The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear＇scale is employed. The dynamic characteristics and non- collocated situation have long been the source of difficulties in motion and vibration control, and deterio- rate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based sub- structure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier （MCM） and an intelligent adaptive tuning algorithm （ATA）, is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network （ANN） as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive＇s dynamic variation. Numerical verification of the effectiveness and robustness of the orooosed strategy is shown for a real feed drive.

Novel pinning control strategies for synchronisation of complex networks with nonlinear coupling dynamics

This paper considers the global stability of controlling an uncertain complex network to a homogeneous trajectory of the uncoupled system by a local pinning control strategy. Several sufficient conditions are derived to guarantee the network synchronisation by investigating the relationship among pinning synchronisation, network topology, and coupling strength. Also, some fundamental and yet challenging problems in the pinning control of complex networks are discussed： （1） what nodes should be selected as pinned candidates？ （2） How many nodes are needed to be pinned for a fixed coupling strength？ Furthermore, an adaptive pinning control scheme is developed. In order to achieve synchronisation of an uncertain complex network, the adaptive tuning strategy of either the coupling strength or the control gain is utilised. As an illustrative example, a network with the Lorenz system as node self-dynamics is simulated to verify the efficacy of theoretical results.

Adaptive Parallel Particle Swarm Optimization Algorithm Based on Dynamic Exchange of Control Parameters

Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.

Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming data.In the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms.The estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming data.In the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established.The authors conduct extensive numerical experiments to evaluate the performance of the proposed method.These experiments demonstrate the effectiveness of the proposed method and support the theoretical results.An application to the Beijing PM2.5 Dataset is also presented.

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem.To deal with this issue,a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints.These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method.Then,the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption.This problem is solved by an improved hierarchical optimization algorithm,in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm.A numerical simulation is performed,and the results confirm the feasibility and effectiveness of the proposed method.

An adaptive fast fixed-time guidance law with an impact angle constraint for intercepting maneuvering targets

Sliding mode guidance laws based on a conventional terminal sliding mode guarantees only finite-time convergence, which verifies that the settling time is required to be estimated by selecting appropriate initial launched conditions. However, rapid convergence to a desired impact angle within a uniform bounded finite time is important in most practical guidance applications. A uniformly finite-time/fixed-time convergent guidance law means that the convergence（settling） time is predefined independently on initial conditions, that is, a closed-loop convergence time can be estimated a priori by guidance parameters. In this paper, a novel adaptive fast fixed-time sliding mode guidance law to intercept maneuver targets at a desired impact angle from any initial heading angle,with no problems of singularity and chattering, is designed. The proposed guidance law achieves system stabilization within bounded settling time independent on initial conditions and achieves more rapid convergence than those of fixed-time stable control methods by accelerating the convergence rate when the system is close to the origin. The achieved acceleration-magnitude constraints are rigorously enforced, and the chattering-free property is guaranteed by adaptive switching gains.Extensive numerical simulations are presented to validate the efficiency and superiority of the proposed guidance law for different initial engagement geometries and impact angles.

Adaptive integral higher order sliding mode controller for uncertain systems

This paper proposes an adaptive integral higher order sliding mode （HOSM） controller for uncertain sys- tems. Instead of a regular control input, the derivative of the control input is used in the proposed control law. The discon- tinuous sign function in the controller is made to act on the time derivative of the control input. The actual control signal obtained by integrating the derivative control signal is smooth and chattering free. The adaptive tuning law used in the proposed controller eliminates the need of prior knowledge about the upper bound of the system uncertainties. Stability and robustness of the proposed controller are proved by using the classical Lyapunov criterion. Simulation results demonstrate the advantages of the proposed control scheme.