Longwall mining automation horizon control:Coal seam gradient identification using piecewise linear fitting

Horizon control, maintaining the alignment of the shearer's exploitation gradient with the coal seam gradient, is a key technique in longwall mining automation. To identify the coal seam gradient, a geological model of the coal seam was constructed using in-seam seismic surveying technology. By synthesizing the control resolution of the range arm and the geometric characteristics of the coal seam, a gradient identification method based on piecewise linear representation(PLR) is proposed. To achieve the maximum exploitation rate within the shearer's capacity, the control resolution of the range arm is selected as the threshold parameter of PLR. The control resolution significantly influenced the number of line segments and the fitting error. With the decrease of the control resolution from 0.01 to 0.02 m, the number of line segments decreased from 65 to 15, which was beneficial to horizon control. However, the average fitting error increased from 0.055 to 0.14 m, which would induce a decrease in the exploitation rate. To avoid significant deviation between the cutting range and the coal seam, the control resolution of the range arm must be lower than 0.02 m. In a field test, the automated horizon control of the longwall face was realized by coal seam gradient identification.

Practical stabilization of receding-horizon control

The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =f(x j|t , u j|t ) by using the Lyapunov direct method combining with receding horizon control technique, and presented a new condition to guarantee the practical stabilization of the systems. With the proposed results, one can design the optimal controllers easily to practically stabilize the predictive control systems.

Receding Horizon Control-Based Stabilization of Singular Stochastic Systems with State Delay

For a class of discrete-time singular stochastic systems with multi-state delay,the stabilization problem of receding horizon control(RHC)is concerned.Due to the difficulty in solving the proposed optimization problem,the RHC stabilization for such systems has not been solved.By adopting the forward and backward equation technique,the optimization problem is solved completely.A sufficient and necessary condition for the optimization controller to have a unique solution is given when the regularization and pulse-free conditions are satisfied.Based on this controller,an RHC stabilization condition is derived,which is in the form of linear matrix inequality.It is proved that the singular stochastic system with multi-state delay is stable in the mean-square sense under appropriate assumptions when the terminal weighting matrix satisfies the given inequality.Numerical examples show that the proposed RHC method is effective in stabilizing singular stochastic systems with multi-state delay.

Distributed three-dimensional cooperative guidance via receding horizon control

The paper presents a new three-dimensional (3D) cooperative guidance approach by the receding horizon control (RHC) technique. The objective is to coordinate the impact time of a group of interceptor missiles against the stationary target. The framework of a distributed RHC scheme is developed, in which each interceptor missile is assigned its own finite-horizon optimal control problem (FHOCP) and only shares the information with its neighbors. The solution of the local FHOCP is obtained by the constrained particle swarm optimization (PSO) method that is integrated into the distributed RHC framework with enhanced equality and inequality constraints. The numerical simulations show that the proposed guidance approach is feasible to implement the cooperative engagement with satisfied accuracy of target capture. Finally, the computation efficiency of the distributed RHC scheme is discussed in consideration of the PSO parameters, control update period and prediction horizon. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Indirect Radau pseudospectral method for the receding horizon control problem

To solve the receding horizon control （RHC） problem in an online manner, a novel numerical method called the indirect Radau pseudospectral method （IRPM） is proposed in this paper. Based on calculus of variations and the first-order necessary optimality condition, the RHC problem for linear time-varying （LTV） system is transformed into the two-point boundary value problem （TPBVP）. The Radau pseudospectral approximation is employed to discretize the TPBVP into well-posed linear algebraic equations. The resulting linear algebraic equations are solved via a matrix partitioning approach afterwards to obtain the optimal feedback control law. For the nonlinear system, the linearization method or the quasi linearization method is employed to approximate the RHC problem with successive linear approximations. Subsequently, each linear problem is solved via the similar method which is used to solve the RHC problem for LTV system. Simulation results of three examples show that the IRPM is of high accuracy and of high compu- tation efficiency to solve the RHC problem and the stability of closed-loop systems is guaranteed.

Multiple UAVs/UGVs heterogeneous coordinated technique based on Receding Horizon Control (RHC) and velocity vector control

Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.

Receding horizon H_∞ control for constrained time-delay systems

A receding horizon Hoo control algorithm is presented for linear discrete time-delay system in the presence of constrained input and disturbances. Disturbance attenuation level is optimized at each time instant, and the receding optimization problem includes several linear matrix inequality constraints. When the convex hull is applied to denote the saturating input, the algorithm has better performance. The numerical example can verify this result.

H∞ Output Feedback Control of Constrained Systems via Moving Horizon Strategy

这份报纸与致动器浸透为分离时间的系统处理 H 产量反馈控制问题。开始，一条抑制 H 输出反馈控制途径在线性矩阵不平等(LMI ) 的框架被介绍优化。在骚乱精力界限上的某些假设下面，靠近环的 H 性能被完成。而且，动人的地平线策略被用于控制性能的一个联机管理以便靠近环的系统能在意外大骚乱的情况中满足控制限制。驱散限制被导出完成动人的地平线靠近环的系统消散。模拟结果证明抑制 H 控制器在骚乱假设下面有效地工作并且动人的地平线 H 控制器罐头交易自动地在令人满意的控制限制和提高的性能之间。

Differential Evolution Based Receding Horizon Control for UAV Motion Planning in Dynamic Environments

This paper presents online motion planning for UAV(unmanned aerial vehicle) in complex threat field,including both static threats and moving threats,which can be formulated as a dynamic constrained optimal control problem.Receding horizon control(RHC) based on differential evolution(DE) algorithm is adopted.A location-predicting model of moving threats is established to assess the value of threat that UAV faces in flight.Then flyable paths can be generated by the control inputs which are optimized by DE under the guidance of the objective function.Simulation results demonstrate that the proposed method not only generates smooth and flyable paths,but also enables UAV to avoid threats efficiently and arrive at destination safely.

Receding horizon H_∞ control for discrete-time Markovian jump linear systems

Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality （LMI） form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.

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.

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

基于优化动力学模型的路径跟踪控制研究

针对一般路径跟踪模型预测控制器在高速大曲率工况下适应性差的问题,提出了一种基于优化动力学模型的自适应预测时域控制策略。首先,为解决经典动力学模型在较高侧向加速度工况下精度不足的问题,建立了包含侧倾转向和变形转向特性的优化模型,实现了车辆状态的较高精度预测;其次,为解决高速大曲率工况下固定预测时域控制效果不佳的问题,提出了基于二维高斯函数的自适应预测时域策略,以低算法复杂度实现了预瞄距离的实时调整;最后,通过CarSim/Simulink联合仿真实验验证了控制器在双移线道路上的控制效果,结果表明,横向位置峰值误差降低45.1%,横摆角峰值误差降低72.4%,设计的控制器对极限工况有更好的适应性。

Stability of networked control systems with multi-step delay based on time-division algorithm

A new control mode is proposed for a networked control system whose network-induced delay is longer than a sampling period. A time-division algorithm is presented to implement the control and for the mathematical modeling of such networked control system. The infinite horizon controller is designed, which renders the networked control system mean square exponentially stable.Simulation results show the validity of the proposed theory.

An Efficient Constrained Model Predictive Control Algorithm Based on Approximate Computation

The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k , while other future control sequences in the optimization horizon are approximated off line by the linear feedback control sequence, so the on line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.

离散时间平均场随机系统的滚动时域控制

研究了一类离散时间平均场随机时变系统的滚动时域控制(RHC)镇定问题。定义一个新的条件期望型的性能指标;通过利用随机极值原理,得到RHC控制器。基于最优性能指标的单调非增性,得到了使平均场随机系统在RHC控制器下均方镇定的条件,即当2个耦合的Lyapunov型不等式被满足时,由RHC策略控制的系统是均方镇定的。通过数值算例得到在RHC控制器下,其状态轨迹满足渐近均方镇定的条件,即平均场随机系统是渐近均方镇定的,验证了该结论的有效性。

一种管道工程机器人路径跟踪控制方法

为了提高管道工程机器人在野外环境下路径跟踪的精度,通过研究管道工程机器人在受到侧滑扰动情况下的转向特性,建立基于滑动情况下的运动学模型。提出了一种改进的滑膜观测器用于估计机器人的滑移参数,使用遗传算法对滑膜观测器的增益系数进行优化,使得估计结果更加准确。此外,考虑到因外界干扰导致机器人路径跟踪控制精度低的问题,提出一种改进的滚动时域控制器(RecedingHorizon Control,RHC)用于机器人的路径跟踪控制,将模型中的非线性项与线性项都进行离散化,减小计算难度。实验结果表明,所提出的基于滑移参数估计的RHC路径跟踪控制策略能够快速准确的跟踪目标路径,具有更好的跟踪精度。

地形跟随中航迹跟踪模型预测控制方案设计

针对地形跟随飞行中的航迹跟踪问题,设计了航迹跟踪模型预测控制器,并讨论了模型预测控制中时域参数切换的自适应方案设计。首先,结合被控对象的特点,设计了用于实现全局稳定跟踪参考航迹的模型预测航迹控制器,并对控制器展开了稳定性分析。然后,充分发挥模型预测控制的预测能力,进行了航迹跟踪方法和综合误差评价策略设计。同时,通过分析不同航迹段的跟踪需求,设计了自适应调整模型预测控制中时域参数的优化方案。仿真验证结果表明,自适应方案相比固定时域方案航迹跟踪精度更高、飞行颠簸小、稳定性好,预设的评价指标函数能有效评估跟踪性能。