Optimal control strategies for stochastically excited quasi partially integrable Hamiltonian systems

In this paper two different control strategies designed to alleviate the response of quasi partially integrable Hamiltonian systems subjected to stochastic excitation are proposed. First, by using the stochastic averaging method for quasi partially integrable Hamiltonian systems, an n-DOF controlled quasi partially integrable Hamiltonian system with stochastic excitation is converted into a set of partially averaged It^↑o stochastic differential equations. Then, the dynamical programming equation associated with the partially averaged It^↑o equations is formulated by applying the stochastic dynamical programming principle. In the first control strategy, the optimal control law is derived from the dynamical programming equation and the control constraints without solving the dynamical programming equation. In the second control strategy, the optimal control law is obtained by solving the dynamical programming equation. Finally, both the responses of controlled and uncontrolled systems are predicted through solving the Fokker-Plank-Kolmogorov equation associated with fully averaged It^↑o equations. An example is worked out to illustrate the application and effectiveness of the two proposed control strategies.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Global Transmission Dynamics of a Schistosomiasis Model and Its Optimal Control

Drug treatment, snail control, cercariae control, improved sanitation and health education are the effective strategies which are used to control the schistosomiasis. In this paper, we consider a deterministic model for schistosomiasis transmission dynamics in order to explore the role of the several control strategies. The global stability of a schistosomiasis infection model that involves mating structure including male schistosomes, female schistosomes, paired schistosomes and snails is studied by constructing appropriate Lyapunov functions. We derive the basic reproduction number R0 for the deterministic model, and establish that the global dynamics are completely determined by the values of R0. We show that the disease can be eradicated when R0?≤1;otherwise, the system is persistent. In the case where ?R0?>1, we prove the existence, uniqueness and global asymptotic stability of an endemic steady state. Sensitivity analysis and simulations are carried out in order to determine the relative importance of different control strategies for disease transmission and prevalence. Next, optimal control theory is applied to investigate the control strategies for eliminating schistosomiasis using time dependent controls. The characterization of the optimal control is carried out via the Pontryagins Maximum Principle. The simulation results demonstrate that the insecticide is important in the control of schistosomiasis.

Numerical Solution of a Class of Nonlinear Optimal Control Problems Using Linearization and Discretization

In this paper, a new approach using linear combination property of intervals and discretization is proposed to solve a class of nonlinear optimal control problems, containing a nonlinear system and linear functional, in three phases. In the first phase, using linear combination property of intervals, changes nonlinear system to an equivalent linear system, in the second phase, using discretization method, the attained problem is converted to a linear programming problem, and in the third phase, the latter problem will be solved by linear programming methods. In addition, efficiency of our approach is confirmed by some numerical examples.

OPTIMAL CONTROL APPLIED IN AUTOMATIC CLUTCH ENGAGEMENTS OF VEHICLES

Start-up working condition is the key to the research of optimal engagementof automatic clutch for AMT. In order to guarantee an ideal dynamic performance of the clutchengagement, an optimal controller is designed by considering throttle angle, engine speed, gearratio, vehicle acceleration and road condition. The minimum value principle is also introduced toachieve an optimal dynamic performance of the nonlinear system compromised in friction plate wearand vehicle drive quality. The optimal trajectory of the clutch engagement can be described in theform of explicit and analytical expressions and characterized by the deterministic and accuratecontrol strategy in stead of indeterministic and soft control techniques which need thousands ofexperiments. For validation of the controller, test work is carried out for the automated clutchengagements in a commercial car with an traditional mechanical transmission, a hydraulic actuator, agroup of sensors and a portable computer system. It is shown through experiments that dynamicbehaviors of the clutch engagement operated by the optimal control are more effective and efficientthan those by fuzzy control.

Hybrid extended Fourier series for optimal control of nonlinear algebraic dynamical systems

The paper introduces a new method for finding optimal control of algebraic dynamic systems. The structure of algebraic dynamical systems is nonlinear with quadratic and bilinear terms. A new hybrid extended Fourier series is introduced, and state and control variables of the system are expanded by this series. Moreover, properties of new series are presented, and integration and product operational matrices are obtained. Using operational matrices, optimal control of the systems is converted to a set of simultaneous nonlinear algebraic relations. An illustrative example is included to compare our results with those in the literature.

Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO

Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.

A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.

Dynamic Programming to Identification Problems

An identification problem is considered as inaccurate measurements of dynamics on a time interval are given. The model has the form of ordinary differential equations which are linear with respect to unknown parameters. A new approach is presented to solve the identification problem in the framework of the optimal control theory. A numerical algorithm based on the dynamic programming method is suggested to identify the unknown parameters. Results of simulations are exposed.

非线性系统近似最优PD动态补偿控制

本文研究了一类基于动态补偿的非线性系统的近似最优PD控制的问题.用微分方程的逐次逼近理论将非线性系统的最优控制问题转化为求解线性非齐次两点边值序列问题,并提供了从时域最优状态反馈到频域最优PD控制器参数的优化方法,从而获取系统最优的动态补偿网络,设计出最优PD整定参数,给出其实现算法.最后仿真示例将所提出的方法与传统的线性二次型调节器(LQR)逐次逼近方法相比较,表明该方法具有良好的动态性能和鲁棒性.

基于动态线性化方法的自适应PD控制器的设计

传统PD控制对非线性系统和参数时变摄动的控制效果不理想,该文提出一种基于动态线性化方法的自适应PD控制器的设计方法。该方法通过输入输出数据对控制器中的比例增益实时调整,从而获得更强的鲁棒性,改善了动、静态性能。仿真结果表明,所提出的方法对系统的参数摄动具有较好的控制效果。

高空风电系统的优化控制研究进展与挑战

为了实现“碳达峰,碳中和”的战略目标,我国将持续推动可再生能源的高比例发展,构建以新能源为主的新型电力系统.作为可再生的清洁源,风能的开发和利用已成为研究的重要方向.研究表明远离地面的高空中风速更加强劲并且更均匀,风向也更稳定,因此,风力发电的进一步突破可以通过用风筝捕获高空风能来实现.为了确保高空风能系统安全、经济、高效地运行,对其控制系统设计的要求极高.本文阐述了国际上几种主流高空风电技术的发电原理、发展进程以及现状.通过对典型的Yo-Yo式结构进行动力学建模,分析了各类非线性控制技术的原理和特点,具体描述了非线性模型预测控制原理和轨迹跟踪控制的仿真结果.总结了未来高空风能控制技术面临的控制算法计算量大,控制系统可靠性研究缺乏以及智能化水平不高等关键问题.

A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

Design and performance analysis of position-based impedance control for an electrohydrostatic actuation system

Electrohydrostatic actuator（EHA） is a type of power-by-wire actuator that is widely implemented in the aerospace industry for flight control, landing gears, thrust reversers, thrust vector control, and space robots. This paper presents the development and evaluation of positionbased impedance control（PBIC） for an EHA. Impedance control provides the actuator with compliance and facilitates the interaction with the environment. Most impedance control applications utilize electrical or valve-controlled hydraulic actuators, whereas this work realizes impedance control via a compact and efficient EHA. The structures of the EHA and PBIC are firstly introduced. A mathematical model of the actuation system is established, and values of its coefficients are identified by particle swarm optimization. This model facilitates the development of a position controller and the selection of target impedance parameters. A nonlinear proportional-integral position controller is developed for the EHA to achieve the accurate positioning requirement of PBIC. The controller compensates for the adverse effect of stiction, and a position accuracy of 0.08 mm is attained.Various experimental results are presented to verify the applicability of PBIC to the EHA. The compliance of the actuator is demonstrated in an impact test.

四旋翼无人机系统模型补偿最优控制

针对四旋翼无人机系统的时变、模型偏差、受扰等问题,本文提出了一种抗扰最优控制算法.首先给出了一种简单的非线性二次调节器(NLQR),然后引入高精度的补偿函数观测器(CFO),提出了模型补偿二次调节(MCQR)最优控制算法,并给出了单个通道闭环系统的稳定性分析.这是一种不依赖或部分依赖模型的非线性最优控制方法,将CFO估计值实时反馈到NLQR中,以补偿非线性模型偏差和扰动.仿真中,基于CFO的MCQR算法实现了四旋翼无人机位置姿态下的稳定控制,在暂态性能、跟踪稳态精度、抗干扰和时变负载能力方面具有突出的优势,同时,在基于Pixhawk的四旋翼飞行器控制算法开发平台中实验验证了所提控制算法的优越性和有效性.

基于一次风流量动态补偿的协调控制系统优化

针对火电机组协调控制系统中主蒸汽压力响应速度较慢的问题,通过将制粉系统中的部分存粉吹出来改善协调控制系统的控制效果。在传统制粉系统模型基础上考虑一次风流量的影响,建立了基于一次风流量的制粉系统模型,经验证,此模型可以很好地反映一次风流量对于制粉系统的动态特性;在制粉系统改进模型的基础上,设计了一次风流量动态补偿系统,与机组原协调控制系统相结合,构成了基于一次风流量动态补偿的协调控制系统,实现了对于磨煤机内部存粉的利用。仿真结果表明,所设计的系统通过改变一次风流量有效地利用了磨煤机内的存粉,在保证快速响应负荷变化的前提下,对于主蒸汽压力有良好的控制效果,提高了主蒸汽压力的响应速度,改善了协调控制系统的控制品质。

计及动态负荷的电力系统静止无功补偿器(SVC)与发电机励磁控制

基于微分代数控制系统的反馈线性化方法,进一步研究了具有非线性负荷的电力系统中静止无功补偿器(Static var compensator,SVC)和发电机三阶模型的励磁控制,表明具有非线性负荷和SVC装置的NDAS(3)仍可以通过状态反馈精确线性化,从而得到具有代数方程的Brunovsky标准型。提出了具有非线性负荷的电力系统SVC与发电机励磁控制的完全精确线性化设计。该控制方法可以同时满足发电机功角稳定和SVC节点处电压。仿真结果表明该方法具有很好的效果和优越性。

非线性控制和优化系统中的浑沌运动

从非线性动态系统的定性理论出发，研究非线性控制和优化系统中的浑沌运动。首先从拓扑的观点严格定义了浑炖，研究了一类特殊的圆周映射，讨论了Ｌｙｓｐｕｎｏｖ指数及其在浑沌诊断中的应用。然后分别研究了离散采样、反馈延迟及系统优化诱发浑沌的可能性及机理，并据此导出一些有意义的结论。