首页> 外文期刊>Discrete and continuous dynamical systems▼hSeries S >AN EFFICIENT ADJOINT COMPUTATIONAL METHOD BASED ON LIFTED IRK INTEGRATOR AND EXACT PENALTY FUNCTION FOR OPTIMAL CONTROL PROBLEMS INVOLVING CONTINUOUS INEQUALITY CONSTRAINTS
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AN EFFICIENT ADJOINT COMPUTATIONAL METHOD BASED ON LIFTED IRK INTEGRATOR AND EXACT PENALTY FUNCTION FOR OPTIMAL CONTROL PROBLEMS INVOLVING CONTINUOUS INEQUALITY CONSTRAINTS

机译:基于升降IRK积分器的高效伴随计算方法和精确的惩罚功能,以实现涉及连续不等式约束的最优控制问题

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Adjoint methods applied to solve optimal control problems (OCPs) have a restriction that the number of constraints shall be less than that of optimization variables. Otherwise, they are less efficient than the forward methods. This paper proposes an efficient adjoint method to solve OCPs for index-1 differential algebraic systems with continuous-time inequality constraints. The continuous-time inequality constraints are not discretized on time grid but transformed into integrals and penalized in the cost through an exact penalty function. Thus, all the constraints except for box constraints on optimization variables can be removed. Furthermore, a lifted implicit Runge-Kutta (IRK) integrator with adjoint sensitivity propagation is employed to accelerate the function and gradient evaluation procedure. Based on a sensitivity update technique, the number of Newton iterations involved in forward simulation can be reduced to one. Besides this, Lagrange interpolation is applied to approximate the states not on collocation points such that integrals in the penalty function can be evaluated on the same grid for forward simulation. Complexity analysis shows that, for the proposed algorithm, computation involved in the sensitivity propagation is comparable to that of forward one. Numerical simulations on the optimal maneuvering a Delta robot demonstrate that the computational speed of the proposed adjoint algorithm is comparable to that of our previous one, which is based on the lifted IRK integrator and forward sensitivity propagation.
机译:应用于解决最佳控制问题的伴随方法(OCP)的限制是约束的数量应小于优化变量的限制。否则,它们比前向方法效率低。本文提出了一种利用连续时间不等式约束的索引-1差分代数系统的高效伴随方法来解决索引-1差分代数系统。连续时间不等式约束不是在时间网格上离散化,而是转化为集成,并通过确切的惩罚功能以成本处罚。因此,可以删除除了优化变量上的框约束之外的所有约束。此外,采用具有伴随灵敏度传播的提升的隐式runge-kutta(Irk)积分器来加速功能和梯度评估过程。基于灵敏度更新技术,前向仿真中涉及的牛顿迭代的数量可以减少到一个。除此之外,Lagrange插值应用于近似于不上的焊接点的状态,使得可以在相同的网格上评估惩罚功能中的积分以进行前进模拟。复杂性分析表明,对于所提出的算法,敏感性传播中涉及的计算与前进的算法相当。最佳机动的数值模拟达达机器人表明所提出的伴随算法的计算速度与前一个换档算法的计算速度相当,其基于提升的IRK积分器和前进灵敏度传播。

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