控制理论与技术（英文版）/Journal Control Theory and TechnologyCSCDEI
查看更多>>“Journal of Control Theory and Applications”(《控制理论与应用》(英文版))是由国家教育部主管、华南理工大学主办的全国性学术刊物。2003年创刊，双月刊、 A4开本，国内外公开发行。本刊主要报道系统控制科学中具有新观念、新思想的理论研究成果及其在各个领域中，特别是高科技领域中的应用研究成果。本刊设置的栏目主要有：论文、短文、书刊评介、国内外学术动态等。读者对象为从事控制理论与应用研究的科技人员，高校师生及其它有关人员。
查看更多>>摘要：In this paper, optimal switching and control approaches are investigated for switched systems with infinite-horizon cost func-tions and unknown continuous-time subsystems. At first, for switched systems with autonomous subsystems, the optimal solution based on the finite-horizon HJB equation is proposed and a data-driven optimal switching algorithm is designed. Then, for the switched systems with subsystem inputs, a data-driven optimal control approach based on the finite-horizon HJB equation is proposed. The data-driven approaches approximate the optimal solutions online by means of the system state data instead of the subsystem models. Moreover, the convergence of the two approaches is analyzed. Finally, the validity of the two approaches is demonstrated by simulation examples.
查看更多>>摘要：In this paper, a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time (DT) systems based on adaptive dynamic programming (ADP) algorithm. First, an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented system. Next, to obtain the optimal control strategy, the stochastic case is converted into the deterministic one by system transformation, and then an ADP algorithm is proposed with convergence analysis. For the purpose of realizing the ADP algorithm, three back propagation neural networks including model network, critic network and action network are devised to guarantee unknown system model, optimal value function and optimal control strategy, respectively. Finally, the obtained optimal control strategy is applied to the original stochastic system, and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.
查看更多>>摘要：This paper is concerned with the optimal linear quadratic Gaussian (LQG) control problem for discrete time-varying system with multiplicative noise and multiple state delays. The main contributions are twofolds. First, in virtue of Pontryagin's maximum principle, we solve the forward and backward stochastic difference equations (FBSDEs) and show the relationship between the state and the costate. Second, based on the solution to the FBSDEs and the coupled difference Riccati equations, the necessary and sufficient condition for the optimal problem is obtained. Meanwhile, an explicit analytical expression is given for the optimal LQG controller. Numerical examples are shown to illustrate the effectiveness of the proposed algorithm.
查看更多>>摘要：Owing to extensive applications in many fields, the synchronization problem has been widely investigated in multi-agent systems. The synchronization for multi-agent systems is a pivotal issue, which means that under the designed control policy, the output of systems or the state of each agent can be consistent with the leader. The purpose of this paper is to investigate a heuristic dynamic programming (HDP)-based learning tracking control for discrete-time multi-agent systems to achieve synchronization while considering disturbances in systems. Besides, due to the difficulty of solving the coupled Hamilton–Jacobi–Bellman equation analytically, an improved HDP learning control algorithm is proposed to realize the synchronization between the leader and all following agents, which is executed by an action-critic neural network. The action and critic neural network are utilized to learn the optimal control policy and cost function, respectively, by means of introducing an auxiliary action network. Finally, two numerical examples and a practical application of mobile robots are presented to demonstrate the control performance of the HDP-based learning control algorithm.
查看更多>>摘要：In lean combustion mode, exhaust gas ratio (EGR) is a significant factor that affects fuel economy and combustion stability. A proper EGR level is beneficial for the fuel economy; however, the combustion stability (coefficient of variation (COV) in indicated mean effective pressure (IMEP)) deteriorated monotonously with increasing EGR. The aim of this study is to achieve a trade-off between the fuel economy and combustion stability by optimizing the EGR set-point. A cost function (J) is designed to represent the trade-off and reduce the calibration burden for optimal EGR at different engine operating condi-tions. An extremum-seeking (ES) algorithm is adopted to search for the extreme value of J and obtain the optimal EGR at an operating point. Finally, a map of optimal EGR set-value is designed and experimentally validated on a real driving cycle.
查看更多>>摘要：MEMS (micro-electro-mechanical-system) IMU (inertial measurement unit) sensors are characteristically noisy and this presents a serious problem to their effective use. The Kalman filter assumes zero-mean Gaussian process and measurement noise variables, and then recursively computes optimal state estimates. However, establishing the exact noise statistics is a non-trivial task. Additionally, this noise often varies widely in operation. Addressing this challenge is the focus of adaptive Kalman filtering techniques. In the covariance scaling method, the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window. This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy. In addition, the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state. Experimental results show that the root mean square errors for both pith and roll axes are sig-nificantly reduced compared to the conventional noise adaptation method, albeit at a slightly higher computational cost. Specifically, the root mean square pitch errors are 1.1◦ under acceleration and 2.1◦ under rotation, which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.
查看更多>>摘要：The existence of error when compressing nonlinear functions into the coefficients of the characteristic model is known to be a key issue in existing characteristic modeling approaches, which is solved in this work by an error-free compression method. We first define a key concept of relevant states with corresponding compressing methods into their coefficients, where the coefficients are continuous and bounded and the compression is error-free. Then, we give the conditions for decoupling characteristic modeling for MIMO systems, and sequentially, we establish characteristic models for nonlinear systems with minimum phase and relative order two as well as the flexible spacecrafts, realizing the equivalence in the characteristic model theory. Finally, we explicitly explain the reasons for normalization in the characteristic model theory.
查看更多>>摘要：The H∞ output feedback control problem for a class of large-scale nonlinear systems with time delay in both state and input is considered in this paper. It is assumed that the interconnected nonlinearities are limited by constant multiplied by unmeas-ured states, delayed states and external disturbances. Different from existing methods to study the H∞ control of large-scale nonlinear systems, the static gain control technique is utilized to obtain an observer-based output feedback control strategy, which makes the closed-loop system globally asymptotically stable and attenuates the effect of external disturbances. An example is finally carried out to show the feasibility of the proposed control strategy.
查看更多>>摘要：In this paper, the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints. Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem, the decentralized control strategy can be established by a series of optimal control policies. A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem. This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function, respectively. The additional stabilizing term is introduced and an improved weight updating law is derived, which relaxes the requirement of initial admissible control policy. Besides, the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints. The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory. Finally, the effective-ness of the proposed decentralized learning control method is verified by simulation results.
查看更多>>摘要：In this paper, a zero-sum game Nash equilibrium computation problem with a common constraint set is investigated under two time-varying multi-agent subnetworks, where the two subnetworks have opposite payoff function. A novel distributed projection subgradient algorithm with random sleep scheme is developed to reduce the calculation amount of agents in the process of computing Nash equilibrium. In our algorithm, each agent is determined by an independent identically distrib-uted Bernoulli decision to compute the subgradient and perform the projection operation or to keep the previous consensus estimate, it effectively reduces the amount of computation and calculation time. Moreover, the traditional assumption of stepsize adopted in the existing methods is removed, and the stepsizes in our algorithm are randomized diminishing. Besides, we prove that all agents converge to Nash equilibrium with probability 1 by our algorithm. Finally, a simulation example verifies the validity of our algorithm.
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