Time/fuel optimal control of constrained linear discrete systems

A unified approach to solving three common optimal control problems is presented, for linear systems under general constraints. The problems are: (1) the time optimal control problem; (2) the fuel optimal control problem in fixed time; (3) the time optimal control problem with a fuel constraint. A special purpose linear programming algorithm is used. State variable constraints are efficiently handled by a cutting plane algorithm. An example of a sixth order system with two inputs and two state variable constraints illustrates the method as implemented on a personal computer.     

Multivariable norm optimal and parameter optimal iterative learning control: a unified formulation

This article investigates the two paradigms of norm optimal iterative learning control (NOILC) and parameter optimal iterative learning control (POILC) for multivariable (MIMO) ?-input, m-output linear discrete-time systems. The main result is a proof that, despite their algebraic and conceptual differences, they can be unified using linear quadratic multi-parameter optimisation techniques. In particular, whilst POILC has been naturally regarded as an approximation to NOILC, it is shown that the NOILC control law can be generated from a suitable choice of control law parameterisation and objective function in a multi-parameter MIMO POILC problem. The form of this equivalence is used to propose a new general approach to the construction of POILC problems for MIMO systems that approximates the solution of a given NOILC problem. An infinite number of such approximations exist. This great diversity is illustrated by the derivation of new convergent algorithms based on time interval and gradient partition that extend previously published work.     

在逐点状态约束下一个四阶线性系统的时间最优控制

在逐点状态约束下,最优控制问题的求解是很困难的,已有的最大值原理和形态规划理论很难用来求解在逐点状态约束下最优控制问题.本文讨论逐点状态约束下一个四阶线性系统的时间最优控制问题.我们采用转换的方法给出了最优时间与最优控制的具体表达式.     

A rigorous solution of the infinite time interval LQ problem with constant state tracking

A linear quadratic constant state tracking problem is considered over an infinite time horizon. It is shown that the solution cannot be obtained as a limit from a finite time horizon problem, as in general the limiting problem is ill-posed. To obtain a rigorous solution, the problem is split in two natural well-posed subproblems. One optimal control problem addresses the transient and the other optimal control problem concerns the steady state behavior. It is shown that the transient problem and the steady state problem are solved by the same control law.     

移动机器人速度加速度饱和约束下的时间最优控制

针对机器人运动系统中普遍存在的速度和加速度约束, 提出一种满足以上约束的机器人运动时间最优控制方法. 首先, 通过最优条件构造哈密尔顿函数, 根据极小值原理求解时间最优控制; 其次, 通过相轨迹分析, 证明了满足约束的时间最优控制律的形式; 再次, 通过求解最优时间, 将满足约束的时间最优控制律转换成末端时间为最优时间的燃料最优控制律; 最后, 在RoboCup 小型足球机器人上进行对比实验, 验证了该方法在规划与实际上的一致性.     

Minimum‐variance control of astronomical adaptive optic systems with actuator dynamics under synchronous and asynchronous sampling

Adaptive optic (AO) systems are now routinely used in ground‐based telescopes to counter the effects of atmospheric turbulence. A deformable mirror (DM) generates a correction wavefront, which is subtracted from the turbulent wavefront using measurements of the residual phase provided by a wavefront sensor (WFS). Minimizing the variance of the residual phase defines a sampled data control problem combining a continuous time minimum‐variance (MV) performance criterion with a discrete‐time controller. For a fairly general class of linear time‐invariant DM and turbulence WFS models, this control problem can be transformed into an equivalent discrete‐time LQ optimization problem involving a set of (discrete‐time) control‐sufficient statistics of the incoming continuous‐time turbulence. This paper shows how to constructively solve this MV problem in the presence of DM's dynamics, starting from continuous‐time models of DM and turbulence. This result is extended to the case of asynchronous DM/WFS sampling. An illustrative application to optimal control of tip‐tilt turbulent modes for the European extremely large telescope in the presence of first‐order DM's dynamics is presented. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal control problems with multiple characteristic time points in the objective and constraints

In this paper, we develop a computational method for a class of optimal control problems where the objective and constraint functionals depend on two or more discrete time points. These time points can be either fixed or variable. Using the control parametrization technique and a time scaling transformation, this type of optimal control problem is approximated by a sequence of approximate optimal parameter selection problems. Each of these approximate problems can be viewed as a finite dimensional optimization problem. New gradient formulae for the cost and constraint functions are derived. With these gradient formulae, standard gradient-based optimization methods can be applied to solve each approximate optimal parameter selection problem. For illustration, two numerical examples are solved.     

热传导方程的时间最优脉冲控制问题的有限元逼近

时间最优控制问题是一类典型的最优控制问题, 受到研究者的广泛关注. 脉冲控制是一种在工程控制中被广泛应用的控制方式. 偏微分方程描述系统的最优控制问题的数值逼近的收敛性为数值求解方法的可行性提供了定性依据. 本文研究热传导方程的时间最优脉冲控制问题的有限元逼近的收敛性. 通过利用投影算子的特性和系统状态的误差估计, 证明了逼近问题的最优时间收敛到原问题的最优时间. 由此进一步利用原问题最优控制的bangbang性证明了最优控制的收敛性.     

A trajectory‐based method for constructing null controllable regions

The problem of determining a system's set of stabilizable states, the null controllable region (NCR), is intricately related to the problem of determining control Lyapunov functions. In this paper, we address the problem of construction of the NCR for control‐affine nonlinear systems with input constraints. To this end, we explain how the boundary of the NCR is covered by time‐optimal trajectories. To construct the NCR, we employ an algorithm based on Pontryagin's minimum principle, which integrates optimal trajectories of a special smooth system in reverse time from the boundary of an initial NCR estimate. We illustrate this algorithm with linear and nonlinear system examples.     

Optimal control during feedback failure

The problem of controlling a perturbed open loop system so as to keep its performance errors within bounds is considered. The objective is to maximise the time during which performance errors remain below a prescribed ceiling, while the controlled system's parameters are within a specified neighbourhood of their nominal values. It is shown that there is an optimal open loop controller that achieves this objective. Conditions under which the optimal controller generates a bang-bang control input signal are characterised. In general, it is shown that the performance of the optimal controller can always be approximated by a bang-bang signal.     

Optimal Control for Pennes' Bioheat Equation

The problem of Pennes' bioheat boundary control through a skin with its inner medium kept at a constant steady‐state temperature where the outer surface subjected to conductive condition is solved by different methods. Analytical and mild solutions of Pennes' boundary control problem yield a discrete optimization problem for temperature profile at the specific depth point as well as temperature profile at all points of the skin in the final time. The pointwise optimal control problem has been solved by discretized form of the unconstrained optimization problem using analytical solution for Pennes' boundary control problem. The analytical solution has been used to solve the optimal control problems via an unconstrained/constrained optimization in Methods I and II. In Method III first, Pennes' boundary control problem is solved using mild solution by strongly continuous semigroup theory. Then, nonlinear optimization problem with linear constraints is proposed to calculate the corresponding piecewise optimal control function. All methods, have been applied to a homogeneous tissue. Mathematical simulation is done to show the optimal controls for the related states when minimum effort is used.     

An optimal control approach to robust tracking of linear systems

In our early work, we show that one way to solve a robust control problem of an uncertain system is to translate the robust control problem into an optimal control problem. If the system is linear, then the optimal control problem becomes a linear quadratic regulator (LQR) problem, which can be solved by solving an algebraic Riccati equation. In this article, we extend the optimal control approach to robust tracking of linear systems. We assume that the control objective is not simply to drive the state to zero but rather to track a non-zero reference signal. We assume that the reference signal to be tracked is a polynomial function of time. We first investigated the tracking problem under the conditions that all state variables are available for feedback and show that the robust tracking problem can be solved by solving an algebraic Riccati equation. Because the state feedback is not always available in practice, we also investigated the output feedback. We show that if we place the poles of the observer sufficiently left of the imaginary axis, the robust tracking problem can be solved. As in the case of the state feedback, the observer and feedback can be obtained by solving two algebraic Riccati equations.     

自由时间最优控制问题的一种控制向量参数化方法

针对自由时间最优控制问题,提出一种控制向量参数化（CVP）方法.通过引入时间尺度因子,将自由时间最优控制问题转化为固定时间问题,并将终端时刻作为优化参数.基于CVP方法,最优控制问题被转化为一个非线性规划（NLP）问题.建立目标和约束函数的Hamiltonian函数,通过求解伴随方程获得目标和约束函数的梯度,采用序列二次规划（SQP）方法获得问题的数值解.对于控制有切换结构的优化问题,给出了一种网格精细化策略,以提高控制质量.补料分批反应器最优控制问题的仿真实验验证了所提出方法的有效性.     

基于控制周期计算的锌液净化除铜过程优化控制

净化除铜过程是锌直接浸出冶炼工艺中硫酸锌溶液净化的第1个步骤,其效果直接影响着后续工序的正常进行以及最终锌产品的质量.本文针对除铜过程中控制周期与锌粉添加量不确定性,造成出口铜离子浓度波动大及锌粉浪费等问题,研究基于控制周期的除铜过程锌粉添加优化控制方法.首先通过分析反应器中氧化还原电位(oxidation-reduction-potential,ORP)与锌粉的时序变化关系计算除铜过程反应响应时间,然后分析响应时间的统计特性,从而确定锌粉控制周期.在确定控制周期的基础上,采用基于控制周期的固定节点控制参数化方法,从而将最优控制求解问题转化为非线性规划问题.最后,采用状态转移算法对该非线性规划问题进行求解.采集工业现场数据进行实验证明了该优化控制方法的有效性,为类似的净化过程的优化控制提供了新思路.     

UNCERTAIN BANG-BANG CONTROL FOR CONTINUOUS TIME MODEL

By using the equation of optimality in uncertain optimal control, an uncertain bang-bang control problem for a continuous time model is investigated. An example is given to illustrate the result obtained.     

A frequency‐constrained geometric Pontryagin maximum principle on matrix Lie groups

We present a geometric discrete‐time Pontryagin maximum principle (PMP) on matrix Lie groups that incorporates frequency constraints on the control trajectories in addition to pointwise constraints on the states and control actions directly at the stage of the problem formulation. This PMP gives first‐order necessary conditions for optimality and leads to two‐point boundary value problems that may be solved by numerical techniques to arrive at optimal trajectories. We demonstrate our theoretical results with numerical simulations on the optimal trajectory generation of a wheeled inverted pendulum and an attitude control problem of a spacecraft on the Lie group SO(3).     

Necessary conditions for a class of optimal multiprocess with state constraints

In this article, we derive a maximum principle for a special class of free end time optimal control of multiprocesses involving a family of control systems acting in different regions defined by state constraints. We are mainly interested in problems with contiguous time intervals. The main feature of our maximum principle is that it covers the case where some of the regions considered may not be visited. This means that the intervals where the corresponding control systems are active may be reduced to a single point. The derivation of our maximum principle is done by reformulating the optimal multiprocess problem as an equivalent fixed time state constrained optimal control problem. This reformulated problem is also of interest since it provides the means to solve optimal multiprocess problems numerically via the direct method. We illustrate our findings with an example concerning the path planning of an autonomous underwater vehicle (AUV) using a simple kinematic model derived for simulations. We use simplified point‐mass model for the motion of an AUV in a horizontal plane and we assume that the ocean currents are known. We recast this problem as the multiprocess optimal control problem of interest and we study it via simulations presenting computational results partially validated by the maximum principle.     

Optimal Nonlinear Income Taxation with a Two-Dimensional Population; A Computational Approach

We consider the optimal income tax problem when income differences are due to differences in abilities and in preferences between consumption and leisure among individuals. We model this problem as an optimal control problem and develop a numerical method for solving it. The method is based on the expansion of state and control variables in Lagrange series and on a spectral collocation method for approximating state equations. In this way the optimal control problem is reduced to a parameter optimization problem. The problem is difficult to solve, but we managed to do so with some limitations. On the basis of our calculations we conclude that the tax system in the two-dimensional case is more redistributive compared to that obtained from the one-dimensional model.     

Finite‐time formation tracking control for multiple vehicles: A motion planning approach

This paper addresses the finite‐time formation tracking problem for multiple vehicles with dynamics model on SE(3) (the specific Euclidean group of rigid body motions), under the condition that the tracking time is preassigned according to the task requirements. By using Pontryagin's maximum principle on Lie groups, a class of finite‐time optimal tracking control laws are designed for vehicles to track a desired trajectory within a given finite time. Meanwhile, the corresponding cost function is minimized. Furthermore, a tracking‐time lower bound is derived for multi‐vehicle systems with control constraints. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control laws. Copyright © 2015 John Wiley & Sons, Ltd.     

Closed‐form solution to finite‐horizon suboptimal control of nonlinear systems

Finite‐horizon optimal control of input‐affine nonlinear systems with fixed final time is considered in this study. It is first shown that the associated Hamilton–Jacobi–Bellman partial differential equation to the problem is reducible to a state‐dependent differential Riccati equation after some approximations. With a truncation in the control equation, a near optimal solution to the problem is obtained, and the global onvergence properties of the closed‐loop system are analyzed. Afterwards, an approximate method, called Finite‐horizon State‐Dependent Riccati Equation (Finite‐SDRE), is suggested for solving the differential Riccati equation, which renders the origin a locally exponentially stable point. The proposed method provides online feedback solution for controlling different initial conditions. Finally, through some examples, the performance of the resulting controller in finite‐horizon control is analyzed. Copyright © 2014 John Wiley & Sons, Ltd.