Transient response improvement of feedback control systems using hybrid reference control

By means of the internal model principle, an approach to improve the performance of a stabilised closed-loop continuous time linear system based on manipulation of the reference signal is developed. A change in the reference signal is equivalent to implementing an instantaneous change via the choice of a decision vector in the state of the reference signal model, and the analysis of the proposed approach called hybrid reference control (HRC) system is then shown to be equivalent to the analysis of a linear impulsive dynamical system. Conditions for asymptotic stability and convergence of the output tracking error for a HRC system are expressed in term of conditions on the decision vectors. Conditions are then derived which guarantee that the HRC system leads to an improved transient performance compared to a conventional closed-loop control system. In particular, it is shown how decision vectors are chosen so that a HRC system results in a deadbeat response.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Event-driven optimization-based control of hybrid systems with integral continuous-time dynamics

In this paper we introduce a class of continuous-time hybrid dynamical systems called integral continuous-time hybrid automata (icHA) for which we propose an event-driven optimization-based control strategy. Events include both external actions applied to the system and changes of continuous dynamics (mode switches). The icHA formalism subsumes a number of hybrid dynamical systems with practical interest, e.g., linear hybrid automata. Different cost functions, including minimum-time and minimum-effort criteria, and constraints are examined in the event-driven optimal control formulation. This is translated into a finite-dimensional mixed-integer optimization problem, in which the event instants and the corresponding values of the control input are the optimization variables. As a consequence, the proposed approach has the advantage of automatically adjusting the attention of the controller to the frequency of event occurrence in the hybrid process. A receding horizon control scheme exploiting the event-based optimal control formulation is proposed as a feedback control strategy and proved to ensure either finite-time or asymptotic convergence of the closed-loop.     

多自由度拟不可积哈密顿系统的随机分数阶最优控制

推广了适用于分数阶系统控制的随机分数阶最优控制策略,提出了高斯白噪声激励下多自由度拟不可积哈密顿系统以响应最小化为目标的随机分数阶最优控制策略.首先,应用拟不可积哈密顿系统随机平均法,将受控系统简化为关于能量的部分平均伊藤方程.然后,将控制性能指标中关于控制力的部分表示为分数阶形式,结合随机动态规划原理,建立并求解部分平均系统的无界遍历控制的随机动态规划方程,获得了随机分数阶最优控制律.最后,采用一个算例验证了随机分数阶控制策略的控制效果和控制效率.研究表明,随机分数阶最优控制策略对传统的整数阶随机动力学系统同样适用,能比传统的整数阶控制策略取得更好的控制效果.另外,随着激励强度增加,整数阶控制策略的控制效率显著降低;而分数阶控制策略的控制效率虽比整数阶控制策略的控制效率略低,但随着激励强度的增加,分数阶控制策略的控制效率缓慢上升并趋于平稳,可以有效地缓解控制效率与控制效果之间的矛盾.     

针对时变轨迹的非线性仿射系统的鲁棒近似最优跟踪控制

针对非线性连续系统难以跟踪时变轨迹的问题,本文首先通过系统变换引入新的状态变量从而将非线性系统的最优跟踪问题转化为一般非线性时不变系统的最优控制问题,并基于近似动态规划算法(ADP)获得近似最优值函数与最优控制策略.为有效地实现该算法,本文利用评价网与执行网来估计值函数及相应的控制策略,并且在线更新二者.为了消除神经网络近似过程中产生的误差,本文在设计控制器时增加一个鲁棒项;并且通过Lyapunov稳定性定理来证明本文提出的控制策略可保证系统跟踪误差渐近收敛到零,同时也验证在较小的误差范围内,该控制策略能够接近于最优控制策略.最后给出两个时变跟踪轨迹实例来证明该方法的可行性与有效性.     

非线性广义最小方差控制律综述

如何设计简单的控制策略对复杂非线性系统进行控制是控制界还未解决的难题．非线性广义最小方差控制律的提出使得非线性控制器的设计可以基于更为一般的非线性模型，并且控制器易于实现．整个系统包含时滞环节，稳定的非线性输入子系统和一个可以用多项式或者状态空间描述的子系统．通过最小化由误差加权项、状态加权项和输入加权项组成的信号的方差得到优化控制器．在系统为开环稳定的情况下，可用史密斯预估器进行控制．本文首先介绍了非线性广义最小方差控制的发展进程，然后综述了基于状态空间和多项式描述的系统的非线性广义最小方差控制器的设计以及其现状和今后的发展方向．     

A multivariable selftuning regulator to control a double effect evaporator

A multivariable selftuning regulator has been applied in control of a pilot plant double effect evaporator. The selftuning regulator consists of a recursive least squares algorithm combined with a single step optimal control strategy which minimizes a quadratic criterion. The algorithm is very fast and can be adjusted by an external operator in order to obtain satisfactory stationary regulator performance.     

Hybrid optimal control of dry clutch engagement

Lately, with the increasing use of automated manual transmissions (AMT) the engagement control of the dry clutch becomes more important. The engagement control plays a crucial role, since different and conflicting objectives have to be satisfied: preservation of driver comfort, fast engagement and small friction losses. In this paper two optimal control strategies for clutch engagement, based on hybrid control principles, are compared. For developing a useful clutch control scheme, the driveline is modelled as a piecewise linear system. The first control strategy is widely known as explicit MPC. However, it seems that it is not suitable (yet) for this type of problem. The second strategy is a piecewise LQ controller, based on piecewise quadratic Lyapunov functions. Simulation results obtained with both strategies are presented and discussed.     

A bio-inspired pursuit strategy for optimal control with partially constrained final state

We discuss a biologically inspired cooperative control strategy which allows a group of autonomous systems to solve optimal control problems with free final time and partially constrained final state. The proposed strategy, termed “generalized sampled local pursuit” (GSLP), mimics the way in which ants optimize their foraging trails, and guides the group toward an optimal solution, starting from an initial feasible trajectory. Under GSLP, an optimal control problem is solved in many “short” segments, which are constructed by group members interacting locally with lower information, communication and storage requirements compared to when the problem is solved all at once. We include a series of simulations that illustrate our approach.     

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.     

Model predictive control of cooperative vehicles using systematic search approach

This paper describes the guidance law design of a group of autonomous cooperative vehicles using model predictive control. The developed control strategy allows one to find a feasible near optimal control sequence with a short and constant computation delay in all situations. The control strategy takes other vehicles predicted positions into account for cooperation purpose. Numerical simulations are provided where a group of quadrotors must reach several way-points while avoiding obstacles and collisions inside the group. Results obtained using a realistic model of small quadrotors show that the approach could be usable in practice.     

Active fault detection and control: Unified formulation and optimal design

A new unified formulation of the active fault detection and control problem for discrete-time stochastic systems and its optimal solution are proposed. The problem formulation stems from the optimal stochastic control problem and includes important special cases: an active detector and controller, an active detector and input signal generator, and an active detector with a given input signal generator. The optimal solution is derived using the so-called closed loop information processing strategy. This strategy respects the influence of the current decision and/or input on the future behavior of the observed system, allows penalizing future wrong decisions, and improves the quality of fault detection. The proposed formulation and obtained solution also provide better understanding of the active fault detection and its relation to the optimal stochastic control. The results are illustrated in numerical examples.     

Finite‐time optimal control for interconnected nonlinear systems

This work addresses the finite‐time optimal control problem for a class of interconnected nonlinear systems with powers of positive odd rational numbers. A series of homogeneous controllers, which are capable of guaranteeing the local finite‐time stability of the closed‐loop systems, are first developed using the adding one power integrator method and backstepping technique. Then, the nested saturation controllers are further proposed to achieve global finite‐time stability. Furthermore, the corresponding design parameters are optimized, and thus, an optimal controller is obtained. A numerical simulation example is finally given to illustrate the effectiveness of the proposed control strategy.     

Iterative learning control for uncertain systems: Noncausal finite time interval robust control design

In this paper, we present a novel robust Iterative Learning Control (ILC) control strategy that is robust against model uncertainty as given by an additive uncertainty model. The design methodology hinges on ??_∞ optimization, but formulated such that the obtained ILC controller is not restricted to be causal, and inherently operates on a finite time interval. Optimization of the robust ILC (R‐ILC) solution is accomplished for the situation where any information about structure in the uncertainty is discarded, and for the situation where the information about the structure in the uncertainty is explicitly taken into account. Subsequently, the convergence and performance properties of resulting R‐ILC controlled system are analyzed. On an experimental set‐up, we show that the presented R‐ILC control strategy can outperform an existing linear‐quadratic norm‐optimal ILC approach and an existing causal R‐ILC approach based on frequency domain ??_∞ synthesis. Copyright © 2010 John Wiley & Sons, Ltd.     

The problem of LQG optimal control via a limited capacity communication channel

The paper addresses a LQG optimal control problem involving bit-rate communication capacity constraints. A discrete-time partially observed system perturbed by white noises is studied. Unlike the classic LQG control theory, the control signal must be first encoded, then transmitted to the actuators over a digital communication channel with a given bandwidth, and finally decoded. Both the control law and the algorithms of encoding and decoding should be designed to archive the best performance. The optimal control strategy is obtained. It is shown that where the estimator-coder separation principle holds, the controller-coder one fails to be true.     

An integrated state space partition and optimal control method of multi-model for nonlinear systems based on hybrid systems

Multilinear model approach turns out to be an ideal candidate for dealing with nonlinear systems control problem. However, how to identify the optimal active state subspace of each linear subsystem is an open problem due to that the closed-loop performance of nonlinear systems interacts with these subspaces ranges. In this paper, a new systematic method of integrated state space partition and optimal control of multi-model for nonlinear systems based on hybrid systems is initially proposed, which can deal with the state space partition and associated optimal control simultaneously and guarantee an overall performance of nonlinear systems consequently. The proposed method is based on the framework of hybrid systems which synthesizes the multilinear model, produced by nonlinear systems, in a unified criterion and poses a two-level structure. At the upper level, the active state subspace of each linear subsystem is determined under the optimal control index of a hybrid system over infinite horizon, which is executed off-line. At the low level, the optimal control is implemented online via solving the optimal control of hybrid system over finite horizon. The finite horizon optimal control problem is numerically computed by simultaneous method for speeding up computation. Meanwhile, the model mismatch produced by simultaneous method is avoided by using the strategy of receding-horizon. Simulations on CSTR (Continuous Stirred Tank Reactor) confirm that a superior performance can be obtained by using the presented method.     

Improved control using PFC and modified linear quadratic regulator for a kind of nonlinear systems

This paper describes the combination design of predictive functional control (PFC) and optimal linear quadratic (LQ) method for a kind of nonlinear process with output feedback coupling. In many existing control methods for this kind of nonlinear systems, the nonlinear part is either ignored or represented as a rough linear one when corresponding predictive control methods are designed. However, by assuming that the nonlinearity can be ignored or simplified to a linear time-varying part may not lead to the good control performance of subsequent linear control designs. The paper is a further investigation on this kind of systems, in which a procedure of PFC plus a modified optimal LQ control is developed. With respect to the proposed control strategy and the corresponding processes, the closed-loop performance is improved concerning tracking ability and disturbance rejection compared with previous predictive control methods. In addition, the proposed control is easy to implement as it selects a simple structure and a modification of the classical control scheme.     

资源约束系统的控制与调度协同优化方法

对于一类通信约束的网络控制系统, 应用通信序列的概念和混合逻辑动态的构架, 将其建模成一类集成控制和调度的资源约束系统模型. 采用线性矩阵不等式来描述离散周期系统的周期通信序列和H_∞ 控制的协同优化问题, 并用一种递增优化序列算法来求解优化的调度策略及其相应的渐近稳定及r指数稳定的H_∞控制器. 该算法结合线性矩阵不等式凸优化问题解决了调度与控制的协同设计问题, 同时也节省了搜索优化解的计算时间.     

Stationary policies for lower bounds on the minimum average cost of discrete‐time nonlinear control systems

The paper deals with the control problem of discrete‐time nonlinear systems. The main contribution of this note is to present conditions that assure the existence of stationary policies that generate lower bounds for the minimal long‐run average cost. These lower bounds coincide with the optimal solution when a mild convergence assumption holds. To illustrate the results, the paper presents an application for the simultaneous state‐feedback control problem, and the derived strategy is used to design a real‐time simultaneous control for two direct current motor devices. The dynamics of these two devices are written in terms of a nonlinear algebraic matrix recurrence, which in turn represents a particular case for our general nonlinear approach. The optimal gain for the corresponding simultaneous state‐feedback problem is obtained, and such a gain was implemented in a laboratory testbed to control simultaneously the two direct current motors. Copyright © 2013 John Wiley & Sons, Ltd.     

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.