Trajectory optimization and the control of a re-entry vehicle in TAEM phase

This paper investigates the optimal trajectory and the feedback linearization control of a re-entry vehicle during TAEM (terminal area energy management) phase. First, an optimization algorithm with dynamic pressure as the cost function is used to obtain the optimal trajectory in TAEM. This optimal trajectory is considered the reference for ensuring a stable flight path of the re-entry vehicle. The control inputs are the angle of attack and bank angle, which determine the total energy and safety of the re-entry vehicle. Second, feedback linearization is used to design a tracking law in the TAEM phase. This paper validates the optimal solution as the reference trajectory with HAC (heading alignment cylinder) and the tracking performance of the re-entry vehicle onto the reference trajectory by feedback linearization.     

基于神经网络的冗余度机器人动态最优化控制

进行了冗余度机器人动态最优化控制的研究,由于局部优化方法有一定的局限性,研究中采用了全局优化方法,基于全局优化的思想,提出了一种冗余度机器人的动态优化控制算法,由于控制算法在数值求解时的困难性,在算法中应用了神经网络,用其来逼近复杂的非线性函数,使问题求解得以简化,通过仿真验证,此控制算法能够获得良好的动态性能,运算速度快,具有工程应用的可行性。     

Adaptive reference governor for constrained linear systems

This paper presents a novel adaptive reference governor for robust tracking control in constrained linear systems with bounded disturbances. The proposed reference governor achieves a performance improvement over the existing reference governors by virtue of its added feature: adaptability. The design of such an adaptive reference governor involves a nonlinear non-deterministic polynomial time (NP)-hard optimization problem because the solution of the optimization problem must be searched for the infinite number of sequences of disturbance. The SDP relaxation method turns out to allow the nonlinear NP-hard problem to be recast into an SDP, which may be readily solved in polynomial time.     

基于遗传算法的非线性控制系统参数优化方法

本文针对非线性控制系统参数优化问题,结合非线性控制系统理论、最优化理论及遗传算法,提出了一种新的仿真优化方法,该方法利用遗传算法来求解非线性控制系统参数优化问题,由于遗传算法是在解空间的多个区域内进行搜索,能以较大的概率跳出局部最优,因此可技巧以整体最优解。仿真结果表明,该方法是一种有效的非线性控制系统参数优化方法。     

扩展拉格朗日乘子粒子群算法解决工程优化问题

工程上很多优化问题,如容器设计、波纹管、板翅式换热器的结构优化设计等,皆为非线性约束优化设计问题,常采用惩罚函数法处理约束条件;为获得问题最优解,该方法需要合理确定初始惩罚因子,且需要动态惩罚因子无穷大。扩展拉格朗日乘子法是一种改进的惩罚函数法,可以克服惩罚函数法的不足,获得全局最优解,但目前对其研究和应用有限。对拉格朗日乘子法与粒子群算法相结合处理非线性约束问题进行研究,提出惩罚因子更新策略,确定扩展拉格朗日乘子粒子群算法合理的操作过程。标准测试函数结果显示：提出的方法及策略实现了扩展拉格朗日乘子粒子群算法解决非线性约束问题,并得到了问题的全局最优解;其在容器及波纹管系列优化设计中的应用进一步显示,提出的方法在处理非线性约束工程实际问题时,运行稳定可靠,可快捷获得问题的全局最优解或近似最优解。     

Analytical design of optimal trajectory with dynamic load-carrying capacity for cable-suspended manipulator

This paper describes the development of an approach for trajectory planning of cable-suspended parallel robots using optimal control approach. A prototype has been built, and tests have been carried out to verify the theoretical results. This paper briefly illustrates this device and presents some initial tests. The final dynamic equations are organized in a closed form similar to serial manipulator equations. Dynamic load-carrying capacity problem is converted into a trajectory optimization problem which is fundamentally a constrained nonlinear optimization problem. The problem is formulated using optimal control theory, and the ideas are analyzed using Pontryagin’s minimum principle. The optimal solutions are found by solving the corresponding nonlinear two-point boundary value problems. The main objective is to find the manipulator load-carrying capacity in point-to-point task by considering actuator torque while cable forces are positive.     

基于非线性规划的间歇反应过程动态优化算法

采用对数障碍法和外点法将实际间歇反应过程中的约束条件转化为惩罚项生成新的目标函数,将有约束优化问题转化为无约束优化问题;采用非线性规划算法中的操作变量参数化方法,将操作变量的最优解转化为操作变量子函数的线性组合,将无限维最优解问题转化为有限维权值向量的最优估计问题;同时应用Lyapunov理论设计一种动态迭代优化算法,重点讨论了采样时间的取值对优化算法收敛性的影响.最终用于解决靛蓝生产中碱熔间歇反应过程中过程参数的最优分布,从而提高目标产物的最终收率和降低生产成本等.通过碱熔间歇反应动态优化问题作为实例进行仿真试验,实验结果表明所述算法的有效性和可靠性.     

Workload balance and part-transfer minimization in flexible manufacturing systems

Problems related to the flow management of a flexible manufacturing system (FMS) are here formulated in terms of combinatorial optimization. We consider a system consisting of several multitool automated machines, each one equipped with a possibly different tool set and linked to each other by a transportation system for part moving. The system operates with a given production mix.The focused flow-management problem is that of finding the part routings allowing for an optimal machine workload balancing. The problem is formulated in terms of a particular capacity assignment problem.With the proposed approach, a balanced solution can be achieved by routing parts on a limited number of different paths. Such a balancing routing can be found in polynomial time. We also give polynomial-time and-space algorithms for choosing, among all workload-balancing routings, the ones that minimize the global amount of part transfer among all machines.     

点焊机器人轨迹能耗模型及其优化算法研究

提出一种点焊机器人最优能耗轨迹优化方法,将机器人关节空间中的关键点用五次多项式曲线连接,并基于机器人动力学建立了永磁交流伺服电机驱动的点焊机器人轨迹能耗模型。在考虑运动学、动力学及生产周期约束的情况下,采用蜜蜂进化型遗传算法求解了点焊机器人最优能耗轨迹。为保证算法全局寻优和收敛速度,构建了一种非线性适应度函数。仿真结果表明,与之前算法相比,求解效率更高,且最优能耗轨迹较最短时间轨迹能耗明显减少。     

间歇过程PSO SQP混合优化算法研究*

针对SQP算法在求解具有复杂约束的间歇过程优化时容易陷入局部极值点的问题,本文提出一种PSO-SQP混合优化算法。该算法首先采用外点罚函数法将间歇过程有约束的优化问题转换为无约束的优化问题,利用PSO强大的全局搜索能力对其进行求解,并把搜索结果作为SQP搜索初始点,以此弥补SQP全局搜索弱的缺点,再利用SQP良好的局部收敛性和较强的非线性收敛速度对原优化问题进行精细搜索,弥补了PSO局部搜索弱的缺点,通过不断的迭代最终获得优化问题的全局最优解。该算法充分利用了SQP和PSO的优缺点,增强了其对复杂约束优化问题的求解能力。将本文提出的算法用于连续搅拌化学反应系统温度控制中,仿真结果表明产物浓度能够充分逼近期望值,且反应器的温度轨迹收敛,从而验证了该算法的有效性和实用价值。     

Optimization of a multi-link steering mechanism using a continuous genetic algorithm

A steering mechanism is one of the main components of a vehicle that is responsible of controlling the directional wheels. The optimal design of a steering mechanism is not a trivial problem because the equations involved in the modeling are highly nonlinear. Therefore, this paper proposed a method for global optimization using a genetic algorithm for a multi-link steering mechanism, which can also be applied to other problems of optimal synthesis of mechanisms.     

基于GA的NMPC在污水处理pH值控制中的应用

污水处理pH中和反应过程具有高度非线性和滞后性。文中首先介绍了污水处理pH值控制过程及其机理模型,然后对其进行分析研究,提出了1种基于遗传优化方法的非线性预测控制策略。利用遗传算法的全局寻优、参数调节简单等优点,解决非线性优化难的问题。仿真实验说明,该方法能够对设定的pH值进行很好的跟踪,并且响应时间较快,实现了对pH值的有效控制。     

基于强优弱劣机制组合算法的机械结构优化技术

针对当前多极值函数全局寻优问题中,各种优化算法虽具全局寻优能力但不能确保寻到全局最优的状况,在研究混合离散变量优化(MDOD)方法和正交网格法的基础上,提出了新的组合式全局寻优策略,以此来改进的MDOD方法和正交网格法进行有机组合,充分利用MDOD方法的高效率性和正交网格法的稳定性协同寻优。该算法应用于桥式起重机的结构优化,可强化各自的优点,弱化各自的缺点,与其他优化方法相比可更大地降低制造成本。     

Nonlinear predictive control of a drying process using genetic algorithms

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control.     

粒子群算法在工程优化设计中的应用

将粒子群算法与惩罚函数法相结合,建构一种离散粒子群算法,解决工程上非线性约束离散变量优化设计问题。为实现离散变量与连续变量的转化,构造了相应的扩张函数,提出惩罚因子的确定策略。通过容器设计算例验证,粒子群算法方法优于文献所列方法。应用粒子群算法、惩罚函数法及所提出的策略对波纹管工程实例进行优化设计,其单位重量下整体波纹管的补偿量比在用产品提高了79.96%,与理论解接近,进一步证明了离散粒子群算法及策略在处理工程非线性约束离散优化设计问题时的有效性,其为工程上类似优化设计提供借鉴。     

Using higher precision-based response surface designs to determine the optimal process target

The selection of the optimal process target is critically important as it directly affects the defect rate, material cost, scrap and rework costs, and the loss to customers. Within the context of the optimal process target problem, a new model is proposed in this paper and two distinct contributions to the related topic are offered. First, while most research work assumes a given process distribution with a known variance, this paper integrates response surface designs into solving the optimal process target problem, thus removing the need to make assumptions regarding process parameters. Second, typical response surface designs consider second-order fitted functions; however, this paper considers a procedure to include higher-order polynomial terms that will result in higher prediction capabilities, thereby giving a more accurate representation of the true process. A constrained nonlinear optimization scheme is used to facilitate the development of this methodology and a numerical example is provided for illustration.     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

Receding horizon online optimization for torque control of gasoline engines

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.     

一种串联机器人的全局参数优化设计方法

针对串联机器人物理参数方程的非线性导致的只能对串联机器人进行局部优化的问题,提出了一种基于线性矩阵不等式的全局优化方法。该方法从串联机器人机构与控制协同设计的理念出发,通过将非线性动力学方程的求解转化为线性矩阵不等式的优化问题,以消耗能量最小的方式来分析串联机器人指定动作下的动态特性,进而从全局角度来获取质量、几何尺寸、惯量等最优物理参数。用该方法指导4自由度重载机器人样机设计,运行效果验证了该方法的有效性。