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Jiang-Tao Huang

Publications and source records attributed to Jiang-Tao Huang.

3 recordsLinked to original sources

An unconstrained-like control-based dynamic method for optimization problems with simple bounds

The optimization problems with simple bounds are an important class of problems. To facilitate the computation of such problems, an unconstrained-like dynamic method, motivated by the Lyapunov control principle, is proposed. This method employs the anti-windup limited integrator to address the bounds of parameters upon the dynamics for unconstrained problem, and then solves the transformed Initial-value Problems (IVPs) with mature Ordinary Differential Equation (ODE) integration methods. It is proved that when the gain matrix is diagonal, the result is equivalent to that of the general dynamic method which involves an intermediate Quadratic Programming (QP) sub-problem. Thus, the global convergence to the optimal solution is guaranteed without the requirement of the strict complementarity condition. Since the estimation of the right active constraints is avoided and no programming sub-problem is involved in the computation process, it shows higher efficiency than the general dynamic method and other common iterative methods through the numerical examples. In particular, the implementation is simple, and the proposed method is easy-to-use.

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The Direct Shooting Method is a Complete Method

The direct shooting method is a classic approach for the solution of Optimal Control Problems (OCPs). It parameterizes the control variables and transforms the OCP to the Nonlinear Programming (NLP) problem to solve. This method is easy to use and it often introduces less parameters compared with all-variable parameterization method like the Pseudo-spectral (PS) method. However, it is long believed that its solution is not guaranteed to satisfy the optimality conditions of the OCP and the costates are not available in using this method. In this paper, we show that the direct shooting method may also provide the costate information, and it is proved that both the state and the costate solutions converge to the optimal as long as the control variable tends to the optimal, while the parameterized control may approach the optimal control with reasonable parameterization. This gives us the credit for the optimal control computation when employing the direct shooting method.

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Variation Evolving for Optimal Control Computation, a Compact Way

A compact version of the variation evolving method (VEM) is developed in the primal variable space for optimal control computation. Following the idea that originates from the Lyapunov continuous-time dynamics stability theory in the control field, the optimal solution is analogized to the stable equilibrium point of a dynamic system and obtained asymptotically through the variation motion. With the introduction of a virtual dimension, namely the variation time, the evolution partial differential equation (EPDE), which seeks the optimal solution with a theoretical guarantee, is developed for the optimal control problem (OCP) with free terminal states, and the equivalent optimality conditions with no employment of costates are established in the primal space. These conditions show that the optimal feedback control law is generally not analytically available because the optimal control is related to the future states. Since the derived EPDE is suitable to be computed with the semi-discrete method in the field of PDE numerical calculation, the optimal solution may be obtained by solving the resulting finite-dimensional initial-value problem (IVP).

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