Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.

Stabilization of linear time-varying systems with state and input constraints using convex optimization

The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.

Power System State Estimation Solution With Zero Injection Constraints Using Modified Newton Method and Fast Decoupled Method in Polar Coordinate

如何保证零注入节点的注入功率在状态估计结果中严格为0是电力系统状态估计研究中的重要问题。在直角坐标下,由于零注入约束为线性约束,可使用修正牛顿法来有效地解决这一问题。因此,借鉴直角坐标下修正牛顿法的思路,提出了极坐标下的修正牛顿法和修正快速解耦估计。这些方法的计算流程与传统的极坐标下的牛顿法和快速解耦估计非常相似,计算速度与大权重法相当,同时能够保证零注入约束严格满足。仿真结果验证了所得结论。

NECESSARY CONDITIONS FOR OPTIMAL CONTROLS OF SEMILINEAR ELLIPTIC VARIATIONAL INEQUALITIES INVOLVING STATE CONSTRAINT

This paper deals with maximum principle for some optimal control problem governed by some elliptic variational inequalities. Some state constraints are discussed. The basic techniques used here are based on those in [1] and a new penalty functional defined in this paper.

OPTIMAL CONTROL OF HYPERBOLIC H-HEMIVARIATIONAL INEQUALITIES WITH STATE CONSTRAINTS

The optimal control problems of hyperbolic H-hemivariational inequalities with the state constraints and nonnomotone multivalued mapping term are considered.The optimal solutions are obtained.In addition,their approximating problems are also studied.

OPTIMAL CONTROL OF PARABOLIC VARIATIONAL INEQUALITIES WITH STATE CONSTRAINT

The optimal control problem of parabolic variational inequalities with the state constraint and nonlinear, discontinuous nonmonotone multivalued mapping term and its approximating problem are studied, which generalizes some obtained results.

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.

Perceptions of Rice Farmers towards Production Constraints: Case Study of Niger State of Nigeria and Hainan of China

Rice yield is still low in Nigeria despite the ecological advantages. Several challenges has been traced it production. The study intend to investigate if other rice producing nations are faced with similar challenges and at what magnitude and more importantly, what can be learn to improve the rice yield in Nigeria. Based on 2013/2014 survey, a total sample of 400 famers were randomly interviewed;164 from Niger State of Nigeria and 236 from Hainan province of China. The study collate the perception of farmers to rice production constraints categorized into biotic, abiotic and socioeconomics. Biplot analysis was employed to examine multivariate pattern of their perceptions towards production constraints. The multivariate technique simultaneously displaying different yield levels and factor constraints in data matrix providing the inter-unit distances, variance and correlations of variables. According to the study, Niger state farmers identified socioeconomic constraint as the major factors to production and attributed it to lack of or insufficient investment while the Hainan farmers majorly identified abiotic constraints. The study also indicated that great potential remain to further improve rice yield in both regions especially in Nigeria given the appropriate investment on essential inputs. This study is of great use to extension officers more so, given the investment in Africa, policy makers take advantage of the bilateral and multilateral relationship to invest ease transfer of agricultural information and technologies between or among partners.

A Maximum Principle for Smooth Infinite Horizon Optimal Control Problems with State Constraints and with Terminal Constraints at Infinity

Necessary conditions for optimality are proved for smooth infinite horizon optimal control problems with unilateral state constraints (pathwise constraints) and with terminal conditions on the states at the infinite horizon. The aim of the paper is to obtain strong necessary conditions including transversality conditions at infinity, which in many cases lead to a set of candidates for optimality containing only a few elements, similar to what is the case in finite horizon problems. However, strong growth conditions are needed for the results to hold.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

考虑电力网络约束的工业园区虚拟电厂调控边界求解方法

构建以新能源为主体的新型电力系统,亟须挖掘负荷侧灵活调节资源参与电网调控。含电解铝、矿热炉等高耗能负荷的工业园区具备良好的调控潜力,但是考虑园区内部电力网络约束,其精确调控边界求解面临着变量维数高、约束非线性的难题,现有方法不能较好地兼顾计算效率和精度。对此,文中将上述问题抽象为高维非线性状态空间在P-Q耦合平面的投影问题:分别建立考虑工业园区安全运行线性化和非线性约束的调控边界投影求解模型,采用一种新颖的高维状态空间投影算法,通过顶点“搜索-映射”的两步式求解过程,得到工业园区型虚拟电厂调控边界的精确投影。算例结果表明,采用所提方法求解的调控边界可由线性不等式组完全表征,与现有调度系统完全兼容,结合与叠加柔性资源调控能力和传统采样方法的对比,验证了该方法的可行性以及高求解精度和效率。

泵控系统的积分终端滑模位置控制

为提高执行器的响应速度、鲁棒性和控制精度,提出了一种新型控制技术,将扩张状态观测器与反步积分终端滑模预设性能控制器相结合。首先,将非线性干扰观测器与扩张状态观测器融合,实现对状态量、外部干扰和匹配干扰的观测。然后,在控制器中引入积分终端滑模和规定性能约束,以加快执行器在位移误差远离平衡点时的跟踪误差收敛速度,并确保瞬态和稳态位置响应在要求的有界范围内。最后,基于李雅普诺夫方法对控制器进行稳定性分析。结果表明:在跟踪具有外负载干扰的复合轨迹情况下,设计的控制器能保证瞬态性能和规定的跟踪精度,与反步滑模控制器和PID相比,所提出的控制器具有更佳的控制性能。

具有介质访问约束的网络化控制系统H∞状态估计

研究了一类存在介质访问约束和信号量化的网络化控制系统的H∞状态估计问题。首先,考虑到介质访问约束和信号量化的影响,将信号量化误差转化为扇形有界的不确定性,对由于访问约束而未获得网络传输的系统输出采用加权补偿策略,根据网络介质的随机访问机制将网络化系统建模为具有不确定性的马尔可夫跳跃系统。然后,在此基础上借助李雅普诺夫稳定性理论和H∞性能约束条件,利用线性矩阵不等式(linear matrix inequality,LMI)方法给出状态估计器存在的充分条件,所设计的状态估计器使得估计误差系统渐近稳定且具有给定的H∞性能。最后,通过仿真实例验证了该设计方法的有效性。

针对机动目标的三维领从协同制导律

针对多飞行器协同拦截机动目标的实际需求,提出一种三维领从时空协同制导律。在视线方向上,基于齐次度理论和固定时间稳定理论设计一种领从协同制导律,确保从飞行器与领飞行器的相对距离和相对速度在固定时间内趋于一致,并且给出一致性误差收敛时间的上界,实现多飞行器的同时刻拦截。在视线法向上为实现以特定的碰撞角度拦截目标,设计一种固定时间收敛的非奇异终端滑模制导律,实现攻击角度的固定时间收敛,并避免了制导指令奇异引起的控制饱和问题。考虑到目标的机动对制导系统产生的不利影响,引入固定时间扰动观测器对目标加速度进行前馈补偿。运用李雅普诺夫稳定性理论来证明系统的固定时间稳定特性。通过仿真实验验证所设计制导律可以有效控制多飞行器在领从框架下同时打击机动目标,并以期望的终端角度攻击目标。

内部控制、国家审计与国企避税

选取2007-2017年上市中央企业集团为研究对象,实证检验内部控制对国企避税的影响作用以及国家审计监督对两者关系的调节效应。研究结果表明,内部控制具有抑制国企避税的治理功能,国家审计监督会显著增强内部控制对国企避税的抑制作用,说明内部控制和国家审计是深入理解国有企业税负水平及其变化的重要因素。进一步研究发现,内部控制对避税的治理效应在地区税收征管强度小、社会审计质量低和避税激进的国企中更为显著;通过分解内部控制五要素后研究发现,内部控制对避税的抑制效应集中体现在控制活动和信息与沟通两大要素上。机制分析结果表明,内部控制可以通过缓解企业融资约束来达到抑制企业避税的治理目的。

考虑运行状态信息的综合能源系统图强化学习优化调度

“双碳”背景下,异质能源的耦合加剧迫使综合能源系统(integrated energy system, IES)拓扑朝着更复杂、更灵活的方向不断演变。然而,现有优化调度方法对非欧网络拓扑知识及其异质潮流约束考虑不足。针对这一问题,提出一种基于图强化学习的综合能源系统优化调度方法。首先,基于图理论在保证节点多样状态的情况下,将异质能源网络拓扑转换为网络图模型。其次,通过建立基于真实图映射的状态-动作-奖励的框架,利用图强化学习的方法学习图模型的非欧拓扑信息,将异质潮流知识加入系统节点运行状态,从而实现IES的安全优化调度。最后,利用某工业园区的真实数据进行仿真验证,所提方法相对于传统方法有效缓解了节点电压越限的问题。结果表明,所提方法能够在考虑IES真实拓扑运行状态信息和异质潮流安全的情况下实现IES的优化调度。

国有股权参股与小微企业融资约束——基于制度逻辑与效率逻辑的双重视角

基于制度逻辑与效率逻辑,本文使用CMES数据实证研究国有股权参股对缓解小微企业融资约束的作用机制。研究发现,政府股权介入增强了政企利益的关联程度,有助于小微企业降低代理成本和信息不对称,并获得资源保障,而小微企业较少受到双重代理问题的影响,通过与政府建立股权联系可以更有效地将信贷资源投入转化为投资项目产出,具有更显著的制度收益效应。因此,国有股权参股是缓解小微企业融资约束的有效政策工具。

国有股权参股对民营企业ESG表现的影响研究

国有股权参股作为混合所有制改革的一种重要形式,是助力民营企业高质量发展的重要手段。企业践行ESG理念是积极谋求高质量发展的具象行为,随之国有股权参股能否发挥环境、社会与治理效应,并激励民营企业提升ESG表现,成为值得深入探讨的问题。文章利用2009—2022年A股民营上市公司数据,考察了国有股权参股对企业ESG表现的影响。研究发现国有股权参股能够显著提升民营企业ESG表现。进一步的作用机制分析发现,国有股权参股主要通过内部治理机制、资源保障机制有效提升企业的ESG表现。同时网络媒体报道对国有股权参股有着“扩音器”效应,显著增强了国有股权参股对企业ESG表现的促进作用。此外,异质性分析发现,国有股权参股对高污染、高市场竞争程度下的企业ESG表现的促进作用更为显著。研究结果可以为合理布局国有资本提供理论支撑,也可以为民营企业完善ESG战略提供经验数据。

基于RSS_GN RRT算法的狭长空间路径规划

针对RRT算法在狭长空间中存在的收敛速度慢及规划路径不平滑的问题,提出了一种RSS_GN RRT算法。为了提升算法的收敛速度,提出了引导节点导向策略与分区域采样策略,极大地减少了算法对无效区域的搜索;其次,算法引入了采样角度约束策略来提高规划路径的质量,并采用父节点拓展选择的方法有效解决了由角度约束引起的迭代次数增加的问题。此外,算法可根据感知信息进行地图地动态重构并规划避障路径,提高了算法在低速动态环境中的适应性。仿真结果显示,在狭长通道环境中,RSS_GN RRT算法在规划路径的耗时上比RRT、Goal_bias RRT、角度约束下的RRT、Informed RRT*及DR-RRT算法分别减少了77.3%,51.9%,84.7%,98.8%和60.3%。在迭代次数上,相比于上述算法,分别减少了95.9%,92%,98.3%,98.3%和89.5%。路径的平均曲率也分别降低了94.1%,93.2%,88.7%,91%和92.9%。仿真结果证明了RSS_GN RRT算法在提升规划速度和改善路径质量方面具有显著优势。同时,本文采用了阿克曼模型的小车实测了算法的局部避障能力。经测试,小车可对行驶途中出现的障碍物进行合理避让。

Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered system.According to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be applied.Then,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear terms.The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero.Meanwhile,it is shown that the constraint requirement on the system will not be violated during the operation.Finally,two simulation examples are provided to show the effectiveness of the proposed control scheme.