模糊奇异摄动系统H∞滤波

针对一类非线性奇异摄动系统,建立基于T-S模糊模型的模糊奇异摄动系统模型,通过Lyapunov方法和Schur补定理,研究其H∞滤波问题.将系统H∞滤波器设计归结为求解一组与摄动参数ε无关的线性矩阵不等式,从而避免由ε引起数值求解的病态问题.所获得的滤波器使闭环系统渐近稳定并能达到给定的H∞性能指标.该方法适用于标准和非标准非线性奇异摄动系统,仿真实例表明了所提出方法的有效性.     

基于LMI的小时滞饱和系统稳定域估计方法

基于Pade近似变换,将小时滞饱和系统的稳定域估计转化为估计奇异摄动饱和系统的稳定域问题.证明了此奇异摄动饱和系统的稳定域具有可解耦性,并在此基础上建立LMI优化模型并提出小时滞饱和系统稳定域估计的降阶方法.算例仿真验证了方法的正确性和有效性.     

奇异摄动时滞系统次优控制的Chebyshev多项式级数方法

研究奇异摄动时滞系统次优控制的近似设计问题.基于奇异摄动的快慢分解理论,将系统的最优控制问题转化为无时滞快子问题和线性时滞慢子问题;利用Chebyshev多项式级数方法将时滞慢子问题的近似求解问题转化为线性代数方程组的求解问题,进而得到原系统的次优控制律,该控制律由Chebyshev多项式级数的基向量表示.仿真算例表明了该方法的有效性.     

模糊奇异摄动系统动态输出反馈H∞控制

针对一类非线性奇异摄动系统,建立了基于T-S 模糊模型的模糊奇异摄动系统模型．通过李亚普诺夫 方法和Schur 补定理,研究其动态输出反馈H∞控制．将系统动态输出反馈H∞控制器设计归结为求解一组与摄动参 数e 无关的线性矩阵不等式,避免了由e 引起的数值求解的病态问题．所获得的控制器使闭环系统渐近稳定,并达 到了给定的H∞性能指标．该方法适用于标准和非标准非线性奇异摄动系统．仿真实例说明了该方法的有效性     

奇异摄动系统的H_∞控制:基于奇异系统的方法

实际系统的奇异系统模型往往通过忽略系统的奇异摄动系统模型微分项系数矩阵中的小时间参数得到.然而,传统的奇异系统控制器设计很少考虑微分项系数矩阵的摄动.本文拓展了现有奇异系统输出动态反馈H∞控制器的设计,使得当实际闭环系统中存在小时间参数时,仍然能保持稳定并且满足一定的H∞性能指标.仿真算例说明了本文提出方法的有效性.     

线性奇异摄动系统的特征结构配置

本文利用奇异摄动系统快、慢时间尺度特性和组合控制方法,将线性定常奇异摄动系统的 特征结构配置问题等价地转化为由慢、快变子系统各自的特征结构配置问题来解决、并在各阶 精度上进行了近似结果的研究.     

奇异摄动系统的H∞控制: 基于奇异系统的方法

实际系统的奇异系统模型往往通过忽略系统的奇异摄动系统模型微分项系数矩阵中的小时间参数得到.然而, 传统的奇异系统控制器设计很少考虑微分项系数矩阵的摄动. 本文拓展了现有奇异系统输出动态反馈H_∞控制器的设计, 使得当实际闭环系统中存在小时间参数时, 仍然能保持稳定并且满足一定的H_∞性能指标. 仿真算例说明了本文提出方法的有效性.     

一般阻尼动力系统非奇异摄动法

借助矩阵摄动理论,将模态叠加法运用于一般阻尼矩阵的动力学方程求解结构的动响应是一种较为理想的方法.但当系统的外荷载激振频率接近于系统的固有频率时,直接将阻尼矩阵作为摄动矩阵,会使解产生奇异,并导致求解失败或误差过大,这是因为模态坐标下的动力学方程是无阻尼方程.为了解决这一问题,本文考虑在模态坐标的动力学方程中保留一定的阻尼.即将阻尼做分解,代入振动方程,得到不同阶次摄动方程,再将摄动方程变换到模态坐标,即采用非奇异摄动方法.最后通过数值算例,得到一阶、二阶摄动,将其与精确解进行比较.精度明显得到改善,基本趋于精确解.从而验证了本方法的精确性和有效性.     

奇异摄动控制系统:理论与应用

系统地回顾了近年来奇异摄动控制技术的发展,主要包括线性奇异摄动系统的稳定性分析与镇定、最优控制、H_∞控制,非线性奇异摄动系统的镇定、优化控制和基于积分流形的几何方法,以及奇异摄动技术在实际工业,例如机器人领域、航天技术领域和工程工业、制造业等中的成功应用.并指出了这一领域进一步研究的方向.     

受扰奇异摄动时滞组合大系统的近似最优控制

研究一类受扰奇异摄动时滞组合大系统的近似最优控制问题．基于奇异摄动的快慢分解理论．将原组合大系统的最优控制问题分解为组合线性快优化子问题和降阶的受扰时滞组合慢优化子问题．通过采用前馈补偿方法抑制外部扰动，采用参数摄动法求解组合慢优化子问题，得到了系统的前馈反馈组合（FFCC）控制律．通过引入降维扰动观测器解决了FFCC律的物理可实现问题．仿真算例验证了所提出方法的有效性．     

Exact Boundary Condition for Semi-discretized Schrödinger Equation and Heat Equation in a Rectangular Domain

An approach to solve finite time horizon suboptimal feedback control problems for partial differential equations is proposed by solving dynamic programming equations on adaptive sparse grids. A semi-discrete optimal control problem is introduced and the feedback control is derived from the corresponding value function. The value function can be characterized as the solution of an evolutionary Hamilton–Jacobi Bellman (HJB) equation which is defined over a state space whose dimension is equal to the dimension of the underlying semi-discrete system. Besides a low dimensional semi-discretization it is important to solve the HJB equation efficiently to address the curse of dimensionality. We propose to apply a semi-Lagrangian scheme using spatially adaptive sparse grids. Sparse grids allow the discretization of the value functions in (higher) space dimensions since the curse of dimensionality of full grid methods arises to a much smaller extent. For additional efficiency an adaptive grid refinement procedure is explored. The approach is illustrated for the wave equation and an extension to equations of Schrödinger type is indicated. We present several numerical examples studying the effect the parameters characterizing the sparse grid have on the accuracy of the value function and the optimal trajectory.     

Delay optimal control and viscosity solutions to associated Hamilton–Jacobi–Bellman equations

In this article, optimal control problems of differential equations with delays are investigated for which the associated Hamilton–Jacobi–Bellman (HJB) equations are nonlinear partial differential equations with delays. This type of HJB equation has not been previously studied and is difficult to solve because the state equations do not possess smoothing properties. We introduce a new notion of viscosity solutions and identify the value functional of the optimal control problems as the unique solution to the associated HJB equations. An analytical example is given as application.     

Uplink power adjustment in wireless communication systems: a stochastic control analysis

This paper considers mobile to base station power control for lognormal fading channels in wireless communication systems within a centralized information stochastic optimal control framework. Under a bounded power rate of change constraint, the stochastic control problem and its associated Hamilton-Jacobi-Bellman (HJB) equation are analyzed by the viscosity solution method; then the degenerate HJB equation is perturbed to admit a classical solution and a suboptimal control law is designed based on the perturbed HJB equation. When a quadratic type cost is used without a bound constraint on the control, the value function is a classical solution to the degenerate HJB equation and the feedback control is affine in the system power. In addition, in this case we develop approximate, but highly scalable, solutions to the HJB equation in terms of a local polynomial expansion of the exact solution. When the channel parameters are not known a priori, one can obtain on-line estimates of the parameters and get adaptive versions of the control laws. In numerical experiments with both of the above cost functions, the following phenomenon is observed: whenever the users have different initial conditions, there is an initial convergence of the power levels to a common level and then subsequent approximately equal behavior which converges toward a stochastically varying optimum.     

Risk-sensitive control of Markov jump linear systems: Caveats and difficulties

In this technical note, we revisit the risk-sensitive optimal control problem for Markov jump linear systems (MJLSs). We first demonstrate the inherent difficulty in solving the risk-sensitive optimal control problem even if the system is linear and the cost function is quadratic. This is due to the nonlinear nature of the coupled set of Hamilton-Jacobi-Bellman (HJB) equations, stemming from the presence of the jump process. It thus follows that the standard quadratic form of the value function with a set of coupled Riccati differential equations cannot be a candidate solution to the coupled HJB equations. We subsequently show that there is no equivalence relationship between the problems of risk-sensitive control and H ^∞ control of MJLSs, which are shown to be equivalent in the absence of any jumps. Finally, we show that there does not exist a large deviation limit as well as a risk-neutral limit of the risk-sensitive optimal control problem due to the presence of a nonlinear coupling term in the HJB equations.

     

Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach

The Hamilton-Jacobi-Bellman (HJB) equation corresponding to constrained control is formulated using a suitable nonquadratic functional. It is shown that the constrained optimal control law has the largest region of asymptotic stability (RAS). The value function of this HJB equation is solved for by solving for a sequence of cost functions satisfying a sequence of Lyapunov equations (LE). A neural network is used to approximate the cost function associated with each LE using the method of least-squares on a well-defined region of attraction of an initial stabilizing controller. As the order of the neural network is increased, the least-squares solution of the HJB equation converges uniformly to the exact solution of the inherently nonlinear HJB equation associated with the saturating control inputs. The result is a nearly optimal constrained state feedback controller that has been tuned a priori off-line.     

A neural network solution for fixed-final time optimal control of nonlinear systems

In this paper, fixed-final time optimal control laws using neural networks and HJB equations for general affine in the input nonlinear systems are proposed. The method utilizes Kronecker matrix methods along with neural network approximation over a compact set to solve a time-varying HJB equation. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. The results of this paper are demonstrated on an example.     

An ENO-Based Method for Second-Order Equations and Application to the Control of Dike Levels

This work aims to model the optimal control of dike heights. The control problem leads to so-called Hamilton-Jacobi-Bellman (HJB) variational inequalities, where the dike-increase and reinforcement times act as input quantities to the control problem. The HJB equations are solved numerically with an Essentially Non-Oscillatory (ENO) method. The ENO methodology is originally intended for hyperbolic conservation laws and is extended to deal with diffusion-type problems in this work. The method is applied to the dike optimisation of an island, for both deterministic and stochastic models for the economic growth.     

Optimal feedback controls in deterministic two-machine flowshopswith finite buffers

This paper deals with an optimal control problem of deterministic two-machine flowshops. Since the sizes of both internal and external buffers are practically finite, the problem is one with state constraints. The Hamilton-Jacobi-Bellman (HJB) equations of the problem involve complicated boundary conditions due to the presence of the state constraints, and as a consequence the usual “verification theorem” may not work for the problem. To overcome this difficulty, it is shown that any function satisfying the HJB equations in the interior of the state constraint domain must be majorized by the value function. The main techniques employed are the “constraint domain approximation” approach and the “weak-Lipschitz” property of the value functions developed in preceding papers. Based on this, an explicit optimal feedback control for the problem is obtained     

A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

The Hamilton–Jacobi–Bellman (HJB) equation can be solved to obtain optimal closed-loop control policies for general nonlinear systems. As it is seldom possible to solve the HJB equation exactly for nonlinear systems, either analytically or numerically, methods to build approximate solutions through simulation based learning have been studied in various names like neurodynamic programming (NDP) and approximate dynamic programming (ADP). The aspect of learning connects these methods to reinforcement learning (RL), which also tries to learn optimal decision policies through trial-and-error based learning. This study develops a model-based RL method, which iteratively learns the solution to the HJB and its associated equations. We focus particularly on the control-affine system with a quadratic objective function and the finite horizon optimal control (FHOC) problem with time-varying reference trajectories. The HJB solutions for such systems involve time-varying value, costate, and policy functions subject to boundary conditions. To represent the time-varying HJB solution in high-dimensional state space in a general and efficient way, deep neural networks (DNNs) are employed. It is shown that the use of DNNs, compared to shallow neural networks (SNNs), can significantly improve the performance of a learned policy in the presence of uncertain initial state and state noise. Examples involving a batch chemical reactor and a one-dimensional diffusion-convection-reaction system are used to demonstrate this and other key aspects of the method.     

Multi-agent graphical games with input constraints: an online learning solution

This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints. In order to obtain the optimal strategy of each agent, it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman (HJB) equations. It is very difficult to solve HJB equations by the traditional method. The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained. In this paper, an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents. Actually, this algorithm is to find the optimal solution of Bellman equations online. This solution employs a distributed policy iteration process, using only the local information available to each agent. It can be proved that under certain conditions, when each agent updates its own strategy simultaneously, the whole multi-agent system will reach Nash equilibrium. In the process of algorithm implementation, for each agent, two layers of neural networks are used to fit the value function and control strategy, respectively. Finally, a simulation example is given to show the effectiveness of our method.