Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

Research on consensus of multi-agent systems with and without input saturation constraints

In recent years,with the continuous development of multi-agent technology represented by unmanned aerial vehicle(UAV)swarm,consensus control has become a hot spot in academic research.In this paper,we put forward a discrete-time consensus protocol and obtain the necessary and sufficient conditions for the second-order consensus of the second-order multi-agent system with a fixed structure under the condition of no saturation input.The theoretical derivation verifies that the two eigenvalues of the Laplacian of the communication network matrix and the sampling period have an important effect on achieving consensus.Then we construct and verify sufficient conditions to achieve consensus under the condition of input saturation constraints.The results show that consensus can be achieved if velocity,position gain,and sampling period satisfy a set of inequalities related to the eigenvalues of the Laplacian matrix.Finally,the accuracy and validity of the theoretical results are proved by numerical simulations.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Sliding-mode control for a rolling-missile with input constraints

This paper investigates the overload stabilization problem of the rolling-missile subject to parameters uncertainty and actuator saturation. In order to solve this problem, a sliding-mode control(SMC) scheme is technically employed by using the backstepping approach to make the dynamic system stable. In addition,SMC with the tanh-type switching function plays an important role in reducing intrinsic vibration. Furthermore, an auxiliary system(AS) is developed to compensate for nonlinear terms arising from input saturation. Finally, the simulation results provide a solution to demonstrate that the suggested SMC and the AS methodology have advantages of strong tracking capability, anti-interference ability and anti-saturation performance.

Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies

This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results.

Robust predictive control of uncertain intergrating linear systems with input constraints

This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.

Consensus of Multi-Agent Systems with Input Constraints Based on Distributed Predictive Control Scheme

Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications.This paper presents a discrete-time consensus protocol for a class of multi-agent systems with switching topologies and input constraints based on distributed predictive control scheme.The consensus protocol is not only distributed but also depends on the errors of states between agent and its neighbors.We focus mainly on dealing with the input constraints and a distributed model predictive control scheme is developed to achieve stable consensus under the condition that both velocity and acceleration constraints are included simultaneously.The acceleration constraint is regarded as the changing rate of velocity based on some reasonable assumptions so as to simplify the analysis.Theoretical analysis shows that the constrained system steered by the proposed protocol achieves consensus asymptotically if the switching interaction graphs always have a spanning tree.Numerical examples are also provided to illustrate the validity of the algorithm.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

具有H_(∞)性能的输入受限三级倒立摆控制器设计

【目的】针对直线三级倒立摆控制输入受限下的稳摆控制问题,设计了具有H_(∞)性能的输入受限三级倒立摆控制器。【方法】利用线性矩阵不等式(LMI)方法,将控制输入约束问题转化为利用线性矩阵不等式求解优化值的优化问题,经Lyapunov稳定性推导出符合条件的LMI,利用MATLAB中的LMI工具箱求解得到有效的K。进一步在控制输入受限下引入H_(∞)性能指标,使所设计的控制输入受限下的算法具有H_(∞)性能。【结果】仿真结果表明,该控制器对于控制输入受限下的复杂高阶次不稳定系统具有很好的控制效果。【结论】控制输入信号在给定的受限范围内实现了三级倒立摆的稳摆要求,且引入H_(∞)性能指标后控制输入和摆杆波动更小。

考虑输出约束和输入饱和的弹仓自适应控制

针对存在参数不确定性和外部扰动的某链式回转弹仓的位置跟踪控制问题,提出一种自适应控制方案。该方案基于神经模糊系统(Neuro-Fuzzy System,NFS)、非线性扰动观测器(Nonlinear Disturbance Observer,NDO)及障碍Lyapunov函数(Barrier Lyapunov Function,BLF),在实现高精度控制的同时满足输出约束和输入饱和的工程条件。通过BLF的设计,保证弹仓的位移跟踪误差约束在给定范围内。结合神经网络的函数逼近能力和模糊逻辑系统的推理能力,用于估计系统中的不确定性,减小对模型的依赖;两者结合的NFS作为NDO的一部分,进一步补偿估计误差和外部扰动,提高控制性能。此外,控制器设计考虑了运动过程中的执行器输入饱和问题。仿真结果表明,所设计的控制器在3种典型工况以及系统参数变化情况下均可实现弹仓的高精度位置跟踪控制,满足系统的约束条件。

ESG责任履行与企业技术创新提质增效

本文以2009~2022年沪深A股上市企业为研究样本,基于资源依赖理论和信号传递理论,考察了企业ESG履责对企业创新的影响及异质性特征,并对企业技术创新资源投入、资源约束、市场关注在其中的中介传导机制以及数字化转型的调节机制进行了检验。研究发现:(1)企业ESG履责对企业技术创新质量和创新效率均有着显著正向影响,对企业创新有着“提质增效”的重要作用;(2)企业ESG履责能够显著提升企业技术创新资源投入,缓解资源约束,提升市场关注,进而促进企业技术创新质量与创新效率的显著提升,资源投入、资源约束、市场关注在ESG履责对企业创新“提质增效”作用中发挥部分中介作用;(3)数字化转型能够调节ESG履责与企业技术创新间的“提质增效”作用,即企业数字化转型程度越高,企业ESG履责越能促进创新质量与创新效率的提升。

控制方向未知的受限多智能体系统的预设时间模糊控制

综合考虑受控制方向未知、输入受限和状态时延影响的有领导者多智能体系统的编队控制问题,设计基于模糊逻辑系统的预设时间一致性控制策略.为了保证编队输出误差在预设时间内满足预定的约束范围要求,引入预设时间性能函数,构造Lyapunov-Krasovskii(L-K)泛函解决状态时延问题,将外界干扰和L-K泛函的导数中的部分项定义为未知非线性函数,并利用模糊逻辑系统对其进行估计,利用Nussbaum函数和均值定理分别处理控制方向未知和输入受限问题,基于以上设计,提出预设时间模糊控制策略,并通过Lyapunov稳定性理论,分析闭环系统的有界稳定性,数值对比仿真和两级化学反应器应用仿真说明控制方法的有效性.

输入时滞多智能体系统的输入受限一致性控制

针对具有输入幅值约束的输入时滞机械系统,研究基于有向图拓扑的多智能体系统(multi-agent system,MAS)的一致性控制问题。对于只有部分智能体能够接收到理想信号的情况,提出一种观测器,使每个智能体都能观测到理想信号,并证明观测误差的指数收敛性。在控制器设计中,通过将系统的输入积分反馈到控制器中,消除输入延迟的影响;结合输入积分法和双曲正切函数,在解决输入延迟问题的同时限制控制输入幅值,并利用FMINCON函数优化控制参数。最后,利用李雅普诺夫方法证明控制目标的实现和闭环系统的渐近稳定性。仿真结果表明,所提出的观测器和控制器能够有效实现系统的一致性控制。

输入受限乘波体飞行器无估计预设性能控制

针对输入受限的乘波体飞行器跟踪控制问题,提出了一种无估计的预设性能控制方法。首先,针对可能发生的执行器饱和问题,设计了一种新型抗饱和补偿系统。然后,利用补偿系统状态构造新的预设性能转换误差,基于预设性能控制与反演控制的方法,为速度子系统与高度子系统设计了无需估计的低复杂度控制器。该设计方法的优越性在于无需状态估计与神经逼近,显著降低了控制的复杂度与计算量。基于Lyapunov稳定理论证明了所有转换误差与跟踪误差最终一致有界。最后,通过数值仿真验证了所提方法的有效性。

四旋翼无人机鲁棒嵌套饱和控制

针对四旋翼无人机在执行任务中,动力系统受到输入饱和限制,传统嵌套饱和控制算法收敛条件严格、收敛速度慢、稳态精度不高、鲁棒性不强等问题,提出了一种四旋翼无人机鲁棒嵌套饱和控制算法。首先,设计了条件相对放松的嵌套饱和控制算法,降低了设计难度,并且给出了闭环系统的稳定性证明。其次,基于条件放松的嵌套饱和控制算法,引入自耦PD和补偿函数观测器,提升系统的响应速度,稳态精度以及鲁棒性,并利用Lyapunov稳定性原理证明了闭环系统的全局稳定性。最后,将所提算法与传统嵌套饱和控制算法和PD控制算法进行了仿真对比,结果表明:本文提出的算法在暂态性能、稳态性能和鲁棒性上都是最优。

一种控制输入约束下的不确定离散系统非脆弱保性能控制器设计

【目的】针对考虑扰动及摄动情况下的控制输入约束的不确定离散系统,提出了非脆弱保性能的控制方法。【方法】首先,以最小化目标函数为性能指标、控制输入饱和范围为约束条件,从而推导出约束状态下的非脆弱保性能控制律。其次,使用李雅普诺夫方程来构造非线性矩阵不等式。再次,结合Schur补定理和布谷鸟群智能优化算法对不等式进行求解,得到控制输入约束下的非脆弱保性能控制律的参数。最后,通过Quanser三自由度陀螺仪平台进行试验验证。【结果】试验结果表明,本研究所提出方法的稳态误差浮动不超过0.07、跟踪误差不超过0.15。【结论】该方法在面对扰动及摄动情况时具有更高的鲁棒性,对提升三自由度陀螺仪的稳定性及控制精度具有重要意义。

具有输入约束和扰动补偿的四旋翼无人机姿态稳定模型预测控制

为了解决四旋翼无人机飞行过程中的姿态控制问题,考虑受到执行器输入约束和外界未知扰动影响,设计了一种具有输入约束和扰动补偿的四旋翼无人机姿态稳定模型预测控制方法;设计简化的四旋翼无人机姿态动力学模型,降低控制器设计的复杂程度,设计带有输入约束的控制器,模拟饱和输入现象,实现饱和输入下的四旋翼姿态稳定控制;设计风扰观测器,实现对外部未知扰动的估计,有效跟踪外部持续扰动,并由此设计扰动补偿律;围绕代价函数设计带有扰动补偿律的最优控制律,作用于四旋翼姿态系统,实现四旋翼无人机姿态的稳定控制;最后进行数值仿真,设置风扰观测器参数λ_(i)为0.25,预测时域N_(p)为10,控制时域N_(c)为9,仿真测试文章方法与不带观测器的非线性预测控制方法(NMPC),验证文章控制方法的有效性和优越性。

输入受限下自主水下航行器路径跟踪控制

针对欠驱动式自主水下航行器AUV的三维路径跟踪控制问题,设计了一种按设定误差性能指标函数收敛的反演滑模控制器。首先基于航行器的运动学和动力学模型将路径跟踪问题转化为非线性系统的镇定问题;然后,利用反演法和李雅普诺夫稳定性理论逐步修正虚拟控制律,从而使控制器能够保证系统的稳定性和鲁棒性。为解决舵执行器的速率受限的问题,通过调整跟踪误差的收敛过程速度的方法设计了控制器。仿真结果表明,上述控制器能够实现对三维直线路径的精确跟踪,而且明显降低了控制系统执行器的速率要求。