Line of sight controller tuning using Bayesian optimisation: application to a double stage stabilisation platform

The inertial stabilisation of the line of sight of an imager fixed on a mobile carrier is considered in order to acquire good quality images despite the disturbances generated by the carrier. A double stage mechanical stabilisation architecture is proposed, where a second stabilisation stage, based on a piezoelectric actuator, is added to the usual structure. The piezoelectric actuator transfer function and hysteresis are characterised through experiments. In order to design the controllers of both stages, a high-level image quality criterion (the modulation transfer function (MTF)) is considered, together with design constraints on the main variables of interest. The criterion and the constraints are evaluated by realistic simulations based on some input and noise profiles measured on a real-life system. The MTF evaluation being time-consuming, a Bayesian optimisation method specially dedicated to expensive-to-evaluate functions is used to obtain the parameters of the controllers. The obtain experimental results are displayed and their performances discussed.     

Quasi-time-dependent stabilisation for 2-D switched systems with persistent dwell-time

ABSTRACT

This paper is concerned with the stabilisation problem for a class of discrete-time two-dimensional (2-D) switched systems with persistent dwell-time (PDT). The systems are described by the well-known Fornasini-Marchesini local state space (FMLSS) model. The concept of PDT switching signals is introduced herein, and each stage consists of a dwell-time period in which no switching occurs and a persistent period an arbitrary switching occurs. Based on a proper Lyapunov function suitable to the PDT switching, which is both quasi-time-dependent (QTD) and mode-dependent, the QTD state feedback controller is designed to ensure the closed-loop system is exponentially stable. Compared with time-independent criteria, new results are more general and flexible, and have less conservativeness. Finally, two examples are provided to show the effectiveness and potential of our proposed methods.     

Feedback stabilization of nonlinear Hamiltonian control systems

Abstract

Conditions for feedback stabilization of smooth affine nonlinear Hamiltonian systems such as robotic manipulators are given. Some results existing in the literature are unified and extended. Application to nonconservative bilinear systems in elasticity is illustrated.     

Control synthesis for polynomial discrete-time systems under input constraints via delayed-state Lyapunov functions

This paper presents a discrete-time control design methodology for input-saturating systems using a Lyapunov function with dependence on present and past states. The approach is used to bypass the usual difficulty with full polynomial Lyapunov functions of expressing the problem in a convex way. Also polynomial controllers are allowed to depend on both present and past states. Furthermore, by considering saturation limits on the control action, the information about the relationship between the present and past states is introduced via Positivstellensatz multipliers. Sum-of-squares techniques and available semi-definite programming (SDP) software are used in order to find the controller.     

Stability and stabilisation of a class of networked dynamic systems

We investigate the stability and stabilisation of a linear time invariant networked heterogeneous system with arbitrarily connected subsystems. A new linear matrix inequality based sufficient and necessary condition for the stability is derived, based on which the stabilisation is provided. The obtained conditions efficiently utilise the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Moreover, a sufficient condition only dependent on each individual subsystem is also presented for the stabilisation of the networked systems with a large scale. Numerical simulations show that these conditions are computationally valid in the analysis and synthesis of a large-scale networked system.     

机器人系统有限时间自适应迭代学习控制

研究任意初态下，机器人系统的有限时间自适应迭代学习控制方法。引入初始修正吸引子的概念，构造一个含有初始修正项的误差变量。针对定常机器人系统和时变机器人系统，采用Lyapunov-like方法，分别设计迭代学习控制器处理系统中不确定性。并且，采用未含/含限幅学习机制，保证闭环系统各变量的一致有界性和误差变量在整个作业区间一致收敛性。藉以实现跟踪误差在预先指定区间的完全跟踪。仿真结果验证所设计控制方法的有效性。     

On the sensitivity of the linear state regulator†

The sensitivity of the optimal response of a linear system with quadratic performance index to changes in the weighting factors in the performance index is determined. This necessitates finding the sensitivity of the feedback gain matrix K to changes in the weighting factors. It is shown that the sensitivities of K can be obtained as the solution of a set of linear matrix differential equations. Further, for time-invariant systems and an infinite time interval, it is seen that the sensitivities of K are found more simply by solving a set of linear algebraic matrix equations.     

Effect of dry heat stabilisation on the functional properties of rice bran proteins

Rice bran was stabilised by dry heat method at 120 °C for 10–60 min, and then, protein was extracted from stabilised rice bran using weak alkali method. The storage characteristics of stabilised rice bran and the influences of dry heat pretreatment on the physicochemical properties of rice bran protein isolate were also evaluated. The results indicated that dry heat pretreatment could not only prevent rancidity of rice bran effectively, but also improve some functional properties of rice bran proteins, such as emulsifying properties, oil holding capacity, and water holding capacity. However, foaming properties and protein solubility were slightly destroyed because of heating. Rice bran was pretreated at 120 °C for 10 or 20 min and then extracted at pH 9.5, and the protein yields were 50.09% and 46.98%, respectively. Therefore, the dry heat treatment at 120 °C for 10 or 20 min was a suitable alternative process in stabilisation of rice bran.     

Event-triggered control for semi-global stabilisation of systems with actuator saturation

This paper investigates the problem of event-triggered control for semi-global stabilisation of null controllable systems subject to actuator saturation. First, for a continuous-time system, novel event-triggered low-gain control algorithms based on Riccati equations are proposed to achieve semi-global stabilisation. The algebraic Riccati equation with a low-gain parameter is utilised to design both the event-triggering condition and the linear controller; a minimum inter-event time based on the Riccati ordinary differential equation is set a priori to exclude the Zeno behaviour. In addition, the high–low gain techniques are utilised to extend the semi-global results to event-based global stabilisation. Furthermore, for a discrete-time system, novel low-gain and high–low-gain control algorithms are proposed to achieve event-triggered stabilisation. Numerical examples are provided to illustrate the theoretical results.     

A probabilistic approach for video stabilisation in compressed domain

Machine vision systems, which are being extensively used for intelligent transportation applications, such as traffic monitoring and automatic navigation, suffer from image instability caused by environment unstable conditions. On the other hand, by increasing the use of home video cameras which sometimes need to remove unwanted camera movement, which is created by cameraman shaking hands, video stabilisation algorithms are being considered. The video stabilisation process consists of three essential phases: global motion estimation, intentional motion estimation and motion compensation. Motion estimation process is the main time consuming part of global motion estimation phase. Using motion vectors extracted directly from MPEG compressed video, instead of any other special feature, can increase the algorithm generality. In addition, it provides the facility for integrating video stabilisation and video compression subsystems and removing the block matching phase from video stabilisation procedure. Elimination of any iterative outlier removal preprocessing and adaptive selection of motion vectors has increased speed of the algorithm. Although deterministic approaches are faster than the related probabilistic methods, they have essential problems in escaping from local optima. For this purpose, particle filters, the ability of which is considerable when submitted to non-linear systems with non-Gaussian noises, are utilised. Setting the parameters of the particle filter using a fuzzy control system reduces the incorrect intentional camera motion removal. The proposed method is simulated and applied to video stabilisation problem and its high performance on various video sequences is demonstrated.