Continuous Optimal Control Problem Research Articles

Reentry trajectory planning for hypersonic vehicles via an improved sequential convex programming method

The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem. The solution efficiency and convergence properties of the planning algorithm become an essential point when considering the practical application scenarios. In this paper, an augmented-Lagrange-based improved sequential convex programming algorithm is proposed to solve it and achieve superior solutions with higher accuracy and efficiency. Firstly, the mapped Chebyshev pseudo-spectral method is utilized to convert the continuous optimal control problem into a highly approximately discretized one. In this framework, the discretization technique is employed to discretize the states and control variables at the time interval, which leads to the improvement of ill-conditioned differential matrix and better approximation properties of the variables. Moreover, by introducing slack variables, automatically updated penalty parameters and Lagrange multipliers during the convexification processing, the original nonconvex problem is transformed into an augmented-Lagrange-function-formed relaxed convex one. Then, the arrived convex subproblem can be solved iteratively by the matured interior-point method until the predetermined precision is satisfied. Based on this transformation, it can effectively alleviate the numerical difficulty and improve the convergence property of the proposed improved sequential convex programming algorithm. Finally, Rigorous theoretical analysis is conducted to prove that the solution sequence generated by the proposed algorithm could converge to an approximate global solution of the original problem. Numerical simulations are also presented, and it is demonstrated that the solution time of the proposed algorithm is more than 95% less than that of the traditional nonlinear programming methods without losing the merits of high precision.

On the adaptation of the Lagrange formalism to continuous time stochastic optimal control: A Lagrange-Chow redux

We show how the classical Lagrangian approach to solving constrained optimization problems from standard calculus can be extended to solve continuous time stochastic optimal control problems. Connections to mainstream approaches such as the Hamilton-Jacobi-Bellman equation and the stochastic maximum principle are drawn. Our approach is linked to the stochastic maximum principle, but more direct and tied to the classical Lagrangian principle, avoiding the use of backward stochastic differential equations in its formulation. Using infinite dimensional functional analysis, we formalize and extend the approach first outlined in Chow (1992) within a rigorous mathematical setting using infinite dimensional functional analysis. We provide examples that demonstrate the usefulness and effectiveness of our approach in practice. Further, we demonstrate the potential for numerical applications facilitating some of our key equations in combination with Monte Carlo backward simulation and linear regression, therefore illustrating a completely different and new avenue for the numerical application of Chow's methods.

Quaternary Continuous Classical Boundary Optimal Control Problem Dominating by Parabolic System

In this paper, our purpose is to study the quaternary continuous classical boundary optimal control vector problem dominated by a quaternary linear parabolic boundary value problem. Under suitable assumptions and with a given quaternary continuous classical boundary control vector, the existence theorem for a unique quaternary state vector solution to the weak form is stated and demonstrated via the method of Galerkin. Furthermore, the continuity of the Lipschitz operator between the state vector solution to the weak form for the dominating equations and the corresponding are proved. The existence of a quaternary continuous classical boundary optimal control vector is stated and demonstrated under suitable Assumptions. The mathematical formulation for the quaternary adjoint boundary value problem associated with each considered boundary value problem is obtained and the Frѐchet derivative for the objective function is derived. Finally, the necessary conditions for the optimality theorem of the problem are stated and demonstrated.

Approximate Solution for Some Continuous Optimal Control Problems Using Veita-Pell Polynomials

In this paper, Veita-Pell polynomials (VPPs) are first presented with some interesting properties. These properties are utilized to construct a general explicit formula of their operational matrix of derivative. Then an appropriate direct technique is suggested for solving quadratic optimal control problems based on VPPs and the idea of state parameterization algorithm. The resulting performance index optimal value shows the proposed method is able to provide a good treatment with fast convergence. The effectiveness of the presented method is illustrated by solving three numerical examples. The obtained results show that as the number of basis functions VPPs increase the error in the solution by the present method will be decreased and it may exactly close with the analytical one. This is the main modification of the algorithm and this contribution in the using special basis functions in obtaining an approximate solution with minimum number of VPPs and satisfactory accuracy.

Trajectory planning for satellite cluster reconfigurations with sequential convex programming method

This paper presents trajectory planning algorithms for large-scale satellite clusters reconfiguration based on sequential convex programming, and these algorithms consider fuel consumption and collision avoidance. Firstly, the trajectory planning problem is formulated as a nonconvex optimal control problem with nonlinear dynamics and nonconvex path constraints. Secondly, the original nonlinear continuous optimal control problem is transformed into a discrete convex optimization subproblem through linearization and discretization. Collision avoidance strategy between discrete points and obstacle avoidance constraints are considered in the convex subproblem to ensure that the trajectories are collision-free. Thirdly, coupled and decoupled sequential convex programming methods are proposed for rapidly generating collision-free and fuel-efficient trajectories. Finally, comparative numerical simulations are presented to demonstrate that as the number of satellites increases, 94 to 99 percent performance improvement over the pseudo-spectral method is achieved in terms of computational efficiency.

Numerical analysis of a family of simultaneous distributed-boundary mixed elliptic optimal control problems and their asymptotic behaviour through a commutative diagram and error estimates

In this paper, we consider a family of simultaneous distributed-boundary optimal control problems (Pα) on the internal energy and the heat flux for a system governed by a mixed elliptic variational equality with a parameter α>0 (the heat transfer coefficient on a portion of the boundary of the domain) and a simultaneous distributed-boundary optimal control problem (P) governed also by an elliptic variational equality with a Dirichlet boundary condition on the same portion of the boundary. We formulate discrete approximations Phα and Ph of the optimal control problems Pα and (P) respectively, for each h>0 and for each α>0, through the finite element method with Lagrange’s triangles of type 1 with parameter h (the longest side of the triangles). The goal of this paper is to study the convergence of this family of discrete simultaneous distributed-boundary mixed elliptic optimal control problems Phα when the parameters α goes to infinity and the parameter h goes to zero simultaneously. We prove the convergence of the family of discrete problems Phα to the discrete problem Ph when α→+∞, for each h>0, in adequate functional spaces. We study the convergence of the discrete problems Phα and Ph, for each α>0, when h→0+ obtaining a commutative diagram which relates the continuous and discrete simultaneous distributed-boundary mixed elliptic optimal control problems Phα,Pα,Ph and (P) by taking the limits h→0+ and α→+∞ respectively. We also study the double convergence of Phα to (P) when (h,α)→(0+,+∞) which represents the diagonal convergence in the above commutative diagram.

Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.

The Classical Continuous Optimal Control for Quaternary Nonlinear Parabolic Boundary Value Problems with State Vector Constraints

This paper aims to study the quaternary classical continuous optimal control problem consisting of the quaternary nonlinear parabolic boundary value problem, the cost function, and the equality and inequality constraints on the state and the control. Under appropriate hypotheses, it is demonstrated that the quaternary classical continuous optimal control ruling by the quaternary nonlinear parabolic boundary value problem has a quaternary classical continuous optimal control vector that satisfies the equality constraint and inequality state and control constraint. Moreover, mathematical formulation of the quaternary adjoint equations related to the quaternary state equations is discovered, and then the weak form of the quaternary adjoint equations is obtained. Lastly, both the necessary conditions for optimality and sufficient conditions for optimality of the proposed problem are stated and proved. The derivation for the Fréchet derivative of the Hamiltonian is attained.

Dynamic optimization analyses and algorithm design for the parallel heat exchange system in spacecraft

Dynamic optimization of the fluid loop is critical of the active thermal control system (ATCS) for future spacecraft. In this paper, the transient heat current model of the parallel heat exchanger system is constructed by the thermoelectric analogy method to analyze the optimal control problem of dynamic flow allocation. The continuous free time optimal flow control problem is transformed into a fixed-time nonlinear programming problem which can be solved by the sequential quadratic programming (SQP) algorithm through the control variable parameterization (CVP) and time-scaling methods. The simulation showed that the optimized flow allocation significantly reduced the process time required for system heat dissipation. Compared with the non-gradient particle swarm optimization (PSO) algorithm, the SQP method constructed in this paper decreased the optimization index by about 4%∼6% and the calculation time by about one order of magnitude. To diminish the sensitivity of the SQP algorithm to the initial value, this paper simplifies the model according to the system characteristics. Through the Pontryagin extremum principle, it is found and proved that, under the simplified model, the optimal flow rates are a set of time-independent variables, which significantly reduces the difficulty of the optimization problem, according to which a hybrid algorithm is designed that uses the optimal result of the simplified model as the initial value predictor. Compared with the average of using the random initial value, using the estimated initial value decreased the optimization index by more than 2% and the computation time by about 60%, under the same SQP algorithm.

Rapid optimization of continuous trajectory for multi-target exploration propelled by electric sails

As an innovative spacecraft propellantless propulsion technology, the electric sail can generate electric fields that interact with the solar wind, generating continuous propulsive thrust. However, unlike conventional electric thrusters, the thrust vector of the electric sail is constrained, and the attitude cannot be adjusted quickly. Consequently, the position and velocity of each flight trajectory at the connecting node and the direction of propulsion acceleration must be continuous. In this study, the continuous trajectory optimization of the electric sail–based probe in multi-target exploration is investigated. An integrative Bezier shaping approach (IBSA) is proposed to realize the rapid optimization of a multi-target exploration trajectory with continuous acceleration. The relationship between boundary constraints, intermediate constraints, and Bezier basis function coefficients is derived, transforming the original continuous optimal control problem of multiple stages into a problem of nonlinear programming that naturally satisfies the boundary and intermediate constraints. Numerical simulation results demonstrate that the IBSA can obtain a better interplanetary trajectory with a shorter calculation time than that obtained with the traditional Bezier shaping approach using piecewise optimization. Furthermore, the simulation results are successfully applied to the initial value estimation of the Gauss pseudospectral method (GPM). Compared with the GPM, the proposed shaping approach differs from the objective function by approximately 2%–6%, but requires approximately 1.5% of the computational time. During the preliminary mission design stage, the IBSA can be used for rapid feasibility assessments of different electric sail–based probe mission profiles.

The Optimal Control Problem for Triple Nonlinear Parabolic Boundary Value Problem with State Vector Constraints

In this paper, the classical continuous triple optimal control problem (CCTOCP) for the triple nonlinear parabolic boundary value problem (TNLPBVP) with state vector constraints (SVCs) is studied. The solvability theorem for the classical continuous triple optimal control vector CCTOCV with the SVCs is stated and proved. This is done under suitable conditions. The mathematical formulation of the adjoint triple boundary value problem (ATHBVP) associated with TNLPBVP is discovered. The Fréchet derivative of the Hamiltonian" is derived. Under suitable conditions, theorems of necessary and sufficient conditions for the optimality of the TNLPBVP with the SVCs are stated and proved.

Necessary and sufficient optimality condition in one discrete optimal control problem

The article investigates a linear discrete optimal control problem, which is an analogue of the linear continuous optimal control problem of population dynamics. The necessary and sufficient optimality condition is proved.

Warm-started semionline trajectory planner for ship’s automatic docking (berthing)

In the usual framework of control, a reference trajectory is needed as the set point for a feedback controller. This reference trajectory can be generated by solving a trajectory optimization problem. This problem is a continuous optimal control problem (OCP) that is transcribed into a finite-dimensional nonlinear optimization problem (NLP) and solved by SQP. For an underactuated conventional vessel, the mathematical model can be very intricate, hence the NLP itself. This causes significant computational time. This article demonstrates that the balance between the feasibility of the reference trajectory and the computational time can be achieved for an underactuated vessel in a disturbed and restricted environment. This is done by: (1) using an almost-globally optimal offline solution as a warm start in a semionline trajectory optimization to speed up the calculation, (2) including the prediction of wind dynamics, and (3) representing the ship as a rigid body and using a predefined boundary to generate the necessary spatial constraints via a point-in-polygon method that ensure a collision-free trajectory in a nonconvex region. Incorporation of these three things maintains a safe and dynamically feasible trajectory where the warm start gives a considerable computational speedup and better results than that without a warm start.

Rates of convergence for the policy iteration method for Mean Field Games systems

Convergence of the policy iteration method for discrete and continuous optimal control problems holds under general assumptions. Moreover, in some circumstances, it is also possible to show a quadratic rate of convergence for the algorithm. For Mean Field Games, convergence of the policy iteration method has been recently proved in [9]. Here, we provide an estimate of its rate of convergence.

Multi-objective excavation trajectory optimization for unmanned electric shovels based on pseudospectral method

With the proposal of intelligent mines, unmanned electric shovels have become a research hotspot in recent years. In the field of autonomous mining, optimal excavation trajectory planning is a key issue since it has a considerable influence on production efficiency and energy consumption. In this paper, a multi-objective trajectory optimization framework based on pseudospectral method is proposed for excavation trajectory planning in autonomous mining scenarios. First, the machinery-ore coupling dynamics of electric shovel is modeled based on the Lagrange method and a multi-objective optimization model is established. Then, the trajectory optimization model is considered as a continuous Optimal Control Problem (OCP) with multiple constraints, a Radau pseudospectral method is developed to discretize the constraints, states and control variables of the dynamics model at the Legendre-Gauss-Radau collocation points, and the shovel dynamics and objective function are converted to algebraic forms. Finally, the associated Non-Linear Programming (NLP) is solved to obtain the optimal excavation trajectory and optimal control variables. In addition, the mapping relationship between the co-states of the OCP and KKT multipliers of the NLP is derived to assess the optimality of the solutions. The results confirm the effectiveness of applying the proposed framework to produce optimal excavation trajectories for unmanned electric shovels by performing a number of simulation and experimental studies. Moreover, the proposed framework tends to be more capable in terms of excavation time and energy consumption compared with other common approaches.

Optimal control for discrete and continuous stochastic descriptor systems with application to a factory management model

In this paper, optimal control models ruled by discrete and continuous stochastic descriptor systems are investigated in order. These descriptor systems are assumed to be regular and impulse-free. For settling discrete optimal control problems, a recurrence equation is proposed with the help of dynamic programming method. Then, optimal control problems for two types of discrete stochastic descriptor systems are considered and optimal solutions are presented by analytical expressions. To simplify continuous optimal control problems, an equation of optimality is derived according to the principle of optimality, and it reveals the essential connection between optimal value and optimal control. Two numerical examples and a factory management model are provided to illustrate the validness of above results.

Convergence for the optimal control problems using collocation at Legendre-Gauss points

The convergence of the novel Legendre-Gauss method is established for solving a continuous optimal control problem using collocation at Legendre-Gauss points. The method allows for changes in the number of Legendre-Gauss points to meet the error tolerance. The continuous optimal control problem is first discretized into a nonlinear programming problem at Gauss collocations by the Legendre-Gauss method. Subsequently, we prove the convergence of the Legendre-Gauss algorithm under the assumption that the continuous optimal control problem has a smooth solution. Compared with those of the shooting method, the single step method, and the general pseudospectral method, the numerical example shows that the Legendre-Gauss method has higher computational efficiency and accuracy in solving the optimal control problem.

A numerical indirect method for solving a class of optimal control problems

In this paper, a numerical indirect method based on wavelets is proposedfor solving the general continuous time-variant linear quadratic optimal con-trol problem. The necessary optimality conditions are applied to convert themain problem into a boundary value problem, as a dynamic system. Thenew problem, using two discrete schemes, Legendre and Chebyshev wavelets,is changed to a system of algebraic equations. To demonstrate the efficiencyof the proposed method two analytical and two numerical examples are given

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel Harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing Harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem.

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances.

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.