Receding Horizon Control Controller Research Articles

Receding horizon control for continuous‐time mean‐field systems

AbstractIn this paper, we study the receding horizon control (RHC) stabilization of continuous‐time mean‐field system. An explicit RHC controller is obtained by introducing a cost function with two terminal matrices and the conditions that make the system stable are also obtained; that is, when two coupled Lyapunov inequalities are satisfied, the system controlled by RHC is stabilized. Finally, a simulation example is given to verify the effectiveness of the proposed strategy.

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.

Control of Type 1 Diabetes Mellitus using Particle Swarm Optimization driven Receding Horizon Controller

Abstract Receding Horizon Control (RHC), also known as Model Predictive Control (MPC) is one of the most intensively researched areas of control algorithms applied in the artificial pancreas concept. Nevertheless, MPC algorithms have not yet been implemented in commercially available insulin pumps, mainly due to their high computational demand, their less robust nature, and their instability on account of model’s uncertainty. In this paper, we present a robust adjustable RHC. The proposed RHC controller was tested under known food inputs by applying a high degree of parameter uncertainty to the virtual patient implemented in the controller to test the robustness of the architecture. A particle swarm optimization method was applied to tune the controller. The so-called identifiable virtual patient (IVP) model was used in the tests, supplemented with food absorption and continuous glucose monitoring sensor model. The implementation was performed in Julia. The results showed that the proposed RHC is sufficiently robust under high food intake and parameter uncertainty.

Distributed RHC stabilization for discrete‐time large‐scale systems with imposed constraints

SummaryThis paper is concerned with finite horizon distributed stabilization of linear discrete‐time large‐scale systems with imposed constraints. Firstly, by constructing the performance index with two terminal weighted matrices, the necessary and sufficient conditions for stabilizing the system are derived. It is shown that distributed receding horizon control (RHC) can stabilize the discrete time large system with constraints if and only if two novel Lyapunov inequalities on the terminal weighting matrix are satisfied. Secondly, the finite horizon optimal control problem is solved by using the maximum principle (necessary condition), and the Riccati equation satisfying the corresponding distributed control system is derived. Based on the Riccati equation, the explicit optimal distributed RHC controller is obtained. Finally, simulation examples are used to verify the feasibility of distributed RHC to stabilize the system.

Research on Cooperative Trajectory Planning and Tracking Problem for Multiple Carrier Aircraft on the Deck

Based on the concepts of the reciprocal velocity obstacle (RVO) and Dubins methods, an improved Dubins-RVO method that can be applied to the scenario with noncircular obstacles is developed. Based on the Dubins-RVO method and one-sided Symplectic Pseudospectral method, a multiaircraft collaborative trajectory planning method was proposed. The kinematic constraint and terminal boundary conditions can be strictly satisfied in the proposed compound trajectory planning method. In addition, a symplectic pseudospectral receding horizon control (RHC) controller is designed to solve the trajectory tracking problem where the constraints on the state and control variables are considered. The proposed compound trajectory planning method is first applied to a two-aircraft problem. The obtained results demonstrate that compared to the RVO and Dubins-RVO methods, the proposed method can effectively avoid the “dead-lock” phenomenon and generate feasible trajectory efficiently satisfying all constraints. In addition, simulation experiment for four aircrafts is conducted. Smooth trajectories are obtained. Despite the presence of disturbances in the environment, the trajectories can be tracked with high precision using the symplectic pseudospectral RHC controller.

Optimal control of tension and thickness for tandem cold rolling process based on receding horizon control

ABSTRACT Tandem cold rolling is a high-speed, high-efficiency, and complex production process. In some cases, minor interference or disoperation may result in large economic losses. To improve control performance and enhance system robustness, an optimal tension and thickness control method is presented in this paper. The control method is designed based on the receding horizon control (RHC) strategy, which has good tracking performance and strong constraint handling capability. First, a state space model is constructed to describe the tension and thickness of the tandem cold rolling process. Then, according to the system model, a RHC controller is designed and introduced into the control system. Based on field data, model verification and a series of experiments are completed. The results show that the proposed RHC control strategy can effectively reduce the effects of disturbances and has excellent control performance compared with the conventional proportion and integration (PI) control strategy.

Distributed model predictive coverage control for decoupled mobile robots

SUMMARYA distributed coverage control scheme based on the state space model predictive control, which is known as receding horizon control (RHC) for decoupled systems, is presented. An optimal control problem is formulated for a set of decoupled robotic systems where a cost function couples the dynamical behavior of the robots. The coupling is described through a connected graph using a Voronoi diagram, where each robot is a node and the cost and constraints of the optimization problem associated with each robot are a function of its state and of the states of its neighbors. The complexity of the problem is addressed by breaking a centralized receding horizon controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and it computes the local control inputs based only on the position of the robot and that of its neighbors. The stability of the distributed scheme is analyzed and its properties compared with the linear quadratic regulator (LQR) design which has been proposed in the literature. Moreover, the proposed coverage algorithm is also applied to deploy a group of mobile robots in a desired formation pattern. The simulation results are used to illustrate the good performance of the proposed coverage control scheme.

Braking-Penalized Receding Horizon Control of Heavy-Haul Trains

Incorporated with a receding horizon control (RHC) approach, a penalty method is proposed for reducing the energy wasted by braking in the operation of a heavy-haul train. The practical nonlinear model of the train is linearized to design the RHC controller. This controller is then applied to the practical nonlinear dynamics of the train, and its performances are analyzed. In particular, the main focus of this paper is on the impact of the brake penalty on the train's performances. Meanwhile, a fence method is presented to tackle two issues. The first issue is that all the cars in a train cannot be individually controlled due to a limit on the available transmission channels for control systems in a long train. The other issue is that the RHC approach suffers from heavy computation and memory load. Simulations verified that the brake penalty presented in the design can remarkably reduce the energy consumption and in-train forces of a train without sacrificing the velocity tracking performance of the train. Simulations also verified that the fence method is essential to reducing the related computation load when the RHC approach is applied to a long heavy-haul train. Further, it is demonstrated that the fence method can effectively shorten computation time and reduce memory usage without severely jeopardizing the performance of a train.

Differential evolution-based receding horizon control design for multi-UAVs formation reconfiguration

Formation reconfiguration is one of the most complicated problems on multiple uninhabited aerial vehicles (multi-UAVs) co-ordinated control. Based on the differential evolution (DE) algorithm, a novel type of control method using receding horizon control (RHC) is proposed aiming at driving multi-UAVs to form a new flying formation. The global control problem of multi-UAVs formation reconfiguration is transformed into several online local optimization problems within a series of receding horizons, while the DE algorithm is adopted to optimize the control sequences for each UAV. The working process of the RHC controller is presented in detail. Finally, two simulation experiments are performed, and simulation results show the feasibility and effectiveness of our proposed control approach.

Receding horizon control for multi-UAVs close formation control based on differential evolution

Close formation flight is one of the most complicated problems on multi-uninhabited aerial vehicles (UAVs) coordinated control. Based on the nonlinear model of multi-UAVs close formation, a novel type of control strategy of using hybrid receding horizon control (RHC) and differential evolution algorithm is proposed. The issue of multi-UAVs close formation is transformed into several on-line optimization problems at a series of receding horizons, while the differential evolution algorithm is adopted to optimize control sequences at each receding horizon. Then, based on the Markov chain model, the convergence of differential evolution is proved. The working process of RHC controller is presented in detail, and the stability of close formation controller is also analyzed. Finally, three simulation experiments are performed, and the simulation results show the feasibility and validity of our proposed control algorithm.

Decentralized receding horizon control for large scale dynamically decoupled systems

We present a detailed study on the design of decentralized receding horizon control (RHC) schemes for decoupled systems. We formulate an optimal control problem for a set of dynamically decoupled systems where the cost function and constraints couple the dynamical behavior of the systems. The coupling is described through a graph where each system is a node, and cost and constraints of the optimization problem associated with each node are only function of its state and the states of its neighbors. The complexity of the problem is addressed by breaking a centralized RHC controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and computes the local control inputs based only on the states of the node and of its neighbors. We analyze the properties of the proposed scheme and introduce sufficient stability conditions based on prediction errors. Finally, we focus on linear systems and show how to recast the stability conditions into a set of matrix semi-definiteness tests.

Comparison of optimisation based control techniques for the control of a CSTR

This paper presents the implementation of optimisation based control techniques an open loop optimal control (OLOC) and a receding horizon control (RHC), for the control of a continuous stirred tank reactor (CSTR) system with exothermic reactions, which exhibit multiple steady state conditions. This type of control techniques requires the solution of an open loop optimal control problem which consists of an objective function, system constraint and state or manipulated variable constraint. Simulation results have demonstrated that the OLOC controller can only perform well if all model parameters are known exactly and not when model mismatches exist. The RHC controller, on the other hand, gives better results and is much more robust for systems with plant/model mismatches within a certain range.