Adaptive H2/H∞ tracking control for a class of uncertain robotic systems

This paper addresses the problem of designing mixed H₂/H_∞ tracking control for a large class of uncertain robotic systems. Nonlinear H_∞ control theory, H₂ control theory and intelligent adaptive control algorithm are combined to construct a hybrid adaptive/robust H₂/H_∞ tracking control scheme. One adaptive neural network system is constructed to approximate the behaviour of uncertain robot dynamics, and the other adaptive control algorithm is designed to estimate the behaviour of the modelled disturbance. Moreover, a robust H_∞ control algorithm is designed to attenuate the effects of the unmodelled disturbance. Only a set of algebraic matrix Riccati-like equations is required to implement the proposed mixed H₂/H_∞ tracking controller, and so an explicit and closed-form solution is obtained. Consequently, the mixed H₂/H_∞ adaptive/robust tracking controller developed here can be analytically computed and easily implemented. Finally, simulations are presented to illustrate the effectiveness of the proposed control algorithm.     

<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> Control Design Using Dynamic Neural Networks

In this paper, a new approach is investigated for adaptive dynamic neural network-based H _∞ control, which is designed for a class of non-linear systems with unknown uncertainties. Currently, non-linear systems with unknown uncertainties are commonly used to efficiently and accurately express the real practical control process. Therefore, it is of critical importance but a great challenge and still at its early age to design a stable and robust controller for such a process. In the proposed research, dynamic neural networks were constructed to precisely approximate the non-linear system with unknown uncertainties first, a non-linear state feedback H _∞ control law was designed next, then an adaptive weighting adjustment mechanism for dynamic neural networks was developed to achieve H _∞ regulation performance, and last a recurrent neural network was employed as a neuro-solver to efficiently and numerically solve the standard LMI problem so as to obtain the appropriate control gains. Finally, case studies further verify the feasibility and efficiency of the proposed research.     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

Implementation of FIR control for H ∞ output feedback stabilisation of linear systems

In this article, finite impulse response (FIR) control is addressed for H _∞ output feedback stabilisation of linear systems. The problem we deal with is the construction of an output feedback controller with a certain FIR structure such that the resulting closed-loop system is asymptotically stable and a prescribed H _∞ norm bound constraint is guaranteed. Some solvability conditions are suggested in this article. Sufficient conditions are derived to obtain a suboptimal solution of the H _∞ FIR control problem via convex optimisation. Also, an equivalent condition for the existence of H _∞ FIR control is presented in the set of linear matrix inequalities (LMIs) and a reciprocal matrices equality constraint. An effective computational algorithm involving LMIs is suggested to solve a concave minimisation problem characterising a local optimal solution of the H _∞ FIR control problem. Numerical examples demonstrate the validity of the proposed H _∞ FIR control and the numerical efficiency of the proposed algorithm for FIR control.     

Multi-log2N交换网络的性能分析模型及控制算法

高速多平面交换网络解决了其内部冲突问题,但需要相应的路由控制算法的辅助,否则,内部冲突不能彻底解决.这是因为包在输入级路由平面的选择不够恰当,容易导致路由冲突的产生.因此,根据冲突链路集的思想,给出一种Multi-log₂N交换网络的控制算法.该算法控制分组在路由平面间的选择,不仅能够适用于RNB和SNB,还能实现单播和多播的控制,保障Multi-log₂N完全实现无阻塞.另一方面,Multi-log₂N消除了内部的链路冲突,提高了交换速率,但对其交换性能缺乏系统的理论分析.给出一种基于嵌入式马尔可夫链的分析模型,对Multi-log₂N网络中队列的使用及分组在队列中的平均等待时间、平均队长等相关性能指标进行了系统的分析,为基于Multi-log₂N的光交换节点的设计提供了良好的理论依据.     

Mixed H2/H∞ robust model predictive control with saturated inputs

In this paper, we investigate the mixed H₂/H_∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.     

Reliable Location and Regression Estimates with Application to Range Image Segmentation

Range images provide important sources of information in many three-dimensional robot vision problems such as navigation and object recognition. Many physical factors, however, introduce noise to the discrete measurements in range images, identifying the need to reassess the error distribution in samples taken from real range images. This paper suggests the use of the L _p norms to yield reliable estimates of location and regression coefficients. This particular approach is compared against two commonly used approaches: Equally Weighted Least Squares, which minimizes the L₂ norm; and the Chebychev approximation, which minimizes the L ₁ norm. The problem is a weighted least squares case where the weights are derived from the chosen parameter, p, and its ability to yield a variety of location estimates spanning from the sample mean to the sample median. These two estimates have a wide application in image processing that includes noise removal. This paper will show the problems associated with these two techniques, and suggest experimental solutions to minimize them. A specific operating range is determined in which the L _p norms perform well and a regression module is used in conjunction with a region-growing segmentation algorithm to provide a reliable partition of range images.     

A structure-preserving algorithm for the minimum H ∞ norm computation of finite-time state feedback control problem

The algorithm being developed here is based on the generating function approach for finite-time H _∞ control and application of canonical transformation of linear Hamiltonian system. First, an equivalent finite-time H _∞ control law in terms of the third-type generating function is derived. Then, by using symplectic structure of the Hamiltonian system's state transition matrix, a group of matrix recursive formulae are deduced for the evaluation of the finite-time H _∞ control law. Combining with a matrix singularity testing procedure, this recursive algorithm verifies the existence condition of sub-optimal H _∞ controllers and gives the minimum H _∞ norm of finite-time control systems. Inherited from the canonical transformation, the matrix recursive formulae have a standard symplectic form; this structure-preserving property helps facilitate reliable and effective computation. Numerical results show the effectiveness of the proposed algorithm.     

On the gap between H2 and entropy performance measures in H∞ control design

Recent papers have considered the problem of minimizing an entropy functional subject to an H_∞ performance constraint. Since the entropy is an upper bound for the H₂ cost, there remains a gap between entropy minimization and H₂ minimization. In this paper we consider a generalized cost functional involving both H₂ and entropy aspects. This approach thus provides a means for optimizing H₂ performance within H_∞ control design.     

A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system

In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural network and its application in the fault tolerant control of a robotic system are investigated. The proposed scheme optimizes the gradient type training on basis of three new adaptive parameters, namely, dead-zone learning rate, hybrid learning rate, and normalization factor. The adaptive dead-zone learning rate is employed to improve the steady state response. The normalization factor is used to maximize the gradient depth in the training, so as to improve the transient response. The hybrid learning rate switches the training between the back-propagation and the real-time recurrent learning mode, such that the training is robust stable. The weight convergence and L ₂ stability of the algorithm are proved via Lyapunov function and the Cluett’s law, respectively. Based upon the theoretical results, we carry out simulation studies of a two-link robot arm position tracking control system. A computed torque controller is designed to provide a specified closed-loop performance in a fault-free condition, and then the RNN compensator and the robust training algorithm are employed to recover the performance in case that fault occurs. Comparisons are given to demonstrate the advantages of the control method and the proposed training algorithm.     

Design of finite-time H∞ controller for uncertain nonlinear systems and its application

This paper studies the problem of finite-time H _∞ control for strict feedback nonlinear systems with external disturbance. The finite-time stability theory, H _∞ control method, backstepping technique, together with adding a power integrator tool are combined to design a finite-time H _∞ state feedback controller. The obtained controller can make the closed-loop system finite-time convergent, and the influence of the external disturbance is attenuated to a given degree. Two numerical examples are presented to show the effectiveness and feasibility of the proposed method. Meanwhile, the proposed method is also applied to robot manipulators.     

A linear-time algorithm for linearL1 approximation of points

In this paper we present a linear-time algorithm for approximating a set ofn points by a linear function, or a line, that minimizes theL ₁ norm. The algorithmic complexity of this problem appears not to have been investigated, although anO(n ³) naive algorithm can be easily obtained based on some simple characteristics of an optimumL ₁ solution. Our linear-time algorithm is optimal within a constant factor and enables us to use linearL ₁ approximation of many points in practice. The complexity ofL ₁ linear approximation of a piecewise linear function is also touched upon.     

Mixed <Emphasis Type="Italic">H</Emphasis><Subscript>2</Subscript>/<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> filtering for a class of high-speed sampling uncertain systems

The problem of mixed H2/H∞ filtering for polytopic Delta operator systems is investigated. The aim is to design a linear asymptotically stable filter which guarantees that the filtering error system has different performances in different filtering channels. Based on a parameter-dependent Lyapunov function, a new mixed H2/H∞ performance criterion is presented. Upon this performance criterion, a sufficient condition for the full-order mixed H2/H∞ filter is derived in terms of linear matrix inequalities. The filter can be obtained from the solution of a convex optimization problem. The proposed filter design procedure is less conservative than the strategy based on the quadratic stability notion. A numerical example is given to illustrate the feasibility of the proposed approach.     

Mixed H2/H∞ multi-channel linear parameter-varying control in discrete time

This paper develops a new method for the synthesis of linear parameter-varying (LPV) controllers in discrete time. LPV plants under consideration have a linear fractional transformation (LFT) representation. In contrast to earlier results which are restricted to single-objective LPV problems, the proposed method can handle a set of H₂/H_∞ specifications that can be defined channel-wise. This practically attractive extension is derived by using specific transformations of both the Lyapunov and scaling/multiplier variables in tandem with appropriate linearizing transformations of the controller data and of the controller scheduling function. It is shown that the controller gain-scheduling function can be constructed as an affine matrix-valued function in the polytopic coordinates of the scheduled parameter, hence is easily implemented on line. Finally, these manipulations give rise to a tractable and practical LMI formulation of the multi-objective LPV control problem.     

H ∞ predictive control of networked control systems

This article is concerned with the problem of H _∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u _t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u _t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.     

L 2-gain analysis for networked predictive control systems based on switching method

This article deals with the problem of L ₂-gain for a class of networked control systems with time-varying network delay in both forward and feedback channels. An improved network predictive control scheme is proposed to compensate the effects of network delay and data dropout using a switching control strategy. Based on the average dwell time method, the restrictions that the original delay-compensation strategy imposes on the subsystems are relaxed and a sufficient condition for weighted L ₂-gain is developed for a class of switching signal. An example illustrates the effectiveness of the proposed method.     

A probabilistic solution of robust H∞ control problem with scaled matrices

This paper addresses the robust H_∞ control problem with scaled matrices. It is difficult to find a global optimal solution for this non-convex optimisation problem. A probabilistic solution, which can achieve globally optimal robust performance within any pre-specified tolerance, is obtained by using the proposed method based on randomised algorithm. In the proposed method, the scaled H_∞ control problem is divided into two parts: (1) assume the scaled matrices be random variables, the scaled H_∞ control problem is converted to a convex optimisation problem for the fixed sample of the scaled matrix and a optimal solution corresponding to the fixed sample is obtained; (2) a probabilistic optimal solution is obtained by using the randomised algorithm based on a finite number N optimal solutions, which are obtained in part (1). The analysis shows that the worst case complexity of proposed method is a polynomial.     

On stability, -gain and control for switched systems

This paper addresses the issues of stability, L₂-gain analysis and H_∞ control for switched systems via multiple Lyapunov function methods. A concept of general Lyapunov-like functions is presented. A necessary and sufficient condition for stability of switched systems is given in terms of multiple generalized Lyapunov-like functions, which enables derivation of improved stability tests, an L₂-gain characterization and a design method for stabilizing switching laws. A solution to the H_∞ control problem for switched systems is also provided.     

P2-Packing问题参数算法的改进

P₂-Packing问题是一个典型的NP难问题.目前这个问题的最好结果是时间复杂度为O^*(2^5.301k)的参数算法,其核的大小为15k.通过对P₂-packing问题的结构作进一步分析,提出了改进的核心化算法,得到大小为7k的核,并在此基础上提出了一种时间复杂度为O^*(2^4.142k)的参数算法,大幅度改进了目前文献中的最好结果.     

The robust model predictive control based on mixed H2/H∞ approach with separated performance formulations and its ISpS analysis

In practical applications, systems are always influenced by parameter uncertainties and external disturbance. Both the H₂ performance and the H_∞ performance are important for the real applications. For a constrained system, the previous designs of mixed H₂/H_∞ robust model predictive control (RMPC) optimise one performance with the other performance requirement as a constraint. But the two performances cannot be optimised at the same time. In this paper, an improved design of mixed H₂/H_∞ RMPC for polytopic uncertain systems with external disturbances is proposed to optimise them simultaneously. In the proposed design, the original uncertain system is decomposed into two subsystems by the additive character of linear systems. Two different Lyapunov functions are used to separately formulate the two performance indices for the two subsystems. Then, the proposed RMPC is designed to optimise both the two performances by the weighting method with the satisfaction of the H_∞ performance requirement. Meanwhile, to make the design more practical, a simplified design is also developed. The recursive feasible conditions of the proposed RMPC are discussed and the closed-loop input state practical stable is proven. The numerical examples reflect the enlarged feasible region and the improved performance of the proposed design.