Receding Horizon Control Method Research Articles

A comparative study of entry guidance for Mars robotic and human landing missions

Human desire to explore Mars has persisted throughout history, and especially, it is required to achieve precise and safe landings on the Martian surface. The atmospheric entry phase plays a crucial role in ensuring mission success, and active research on entry guidance methods continues. This paper presents a comparative study between the two primary types of entry guidance methods: (1) path planning-tracking and (2) predictor–corrector methods by applying them to solve two distinct Mars atmospheric entry guidance problems: (a) robotic entry and (b) human entry missions. Optimization approaches are adopted for the path planning-tracking method. After generating an optimal reference trajectory based on a mission concept-defined cost function, the receding horizon control method is employed to minimize tracking errors. In the predictor–corrector method, two parameterized entry guidance approaches are explored and analyzed. A well-known numerical predictor–corrector method that utilizes a parameterized bank angle profile for entry trajectory generation is tested to demonstrate its robustness against random dispersions. On the other hand, another predictor–corrector type guidance method that assumes a polynomial shape to shape an altitude profile over range-to-go is adopted to show its range control capability. Numerical simulations reveal the pros and cons of each type of entry guidance method, and a suitable guidance method for each Mars entry mission is identified depending on the mission concept and requirements. The analysis results provide future work.

An adaptive receding horizon-based flexible mode switching control strategy of parallel hybrid electric vehicles

Mode switching is crucial to efficient drive in variable driving conditions for hybrid electric vehicles (HEVs), thus many scholars are attracted to implement its high-quality control. The receding horizon control method is an attractive method to solve this problem. However, the choice of prediction horizon is arduous due to the contradiction between the optimization performance and the computational cost. Motivated by this issue, this paper proposes an adaptive receding horizon control (RHC) method, which contains optimization layer (OPL) and feedback control layer (FCL). First, an OPL adaptive receding horizon control method is proposed to obtain the optimal control command, in which a novel adaptive factor, considering the proportional between the maximum traction motor torque and powertrain demand torque, is proposed to adjust the prediction horizon length and control constraints. Then, regarding OPL output as the reference trajectory, a lower-RHC method is designed in FCL to track the reference trajectory. Finally, the proposed control method is validated in simulation and bench experiment. Compared with the existing method, the clutch trajectory is optimized by RHC with adaptive prediction horizon and constrains, and the anti-disturbance ability is enhanced by the lower-RHC. The simulation and bench experiment results indicate that the 0–40 km/h acceleration time and jerk are reduced using the proposed method, respectively.

Signal Source Traversal Based on Quadrotors in Environments With Obstacles: A Data-Driven Receding Horizon Control Approach

This paper deals with the problem of signal source traversal based on a quadrotor in an environments with obstacles by proposing a data-driven receding horizon control approach. The proposed control approach includes two parts: One part is a data-driven learning method while the other part is a receding horizon control method. Gaussian process as a kind of data-driven learning method is used to provide the efficient posterior probability distribution of the possible positions for all the signal sources based on the received signal strength data. It should be pointed out that the obstacles in the environment obstruct signal detection and the antennas diverge the direction of signal transmission such that the positions of signal sources are frequently updated. In order to cope with this kind of dynamic events, a receding horizon control approach in the light of a new cost function is proposed to enables the quadrotor to traverse signal sources, where the generated trajectory is smooth and the quadrotor can be guided to efficiently avoid “L” shape obstacles. Finally, simulation and experimental results show that the quadrotor controlled by the proposed data-driven receding horizon control approach can move along the smooth trajectory to avoid obstacles and traverse all the signal sources.

Pigeon-inspired optimisation algorithm with hierarchical topology and receding horizon control for multi-UAV formation

Multi-UAV formation is a crucial research aspect of UAV cooperation control. This paper proposes a hierarchical topology pigeon-inspired optimisation (HPIO) and a receding horizon control (RHC) to deal with the problem. The RHC method can convert the problem into an online optimisation problem. Besides, inspired by the structure of pigeon flock, a new topology with a hierarchical structure of pigeons is designed. The velocity updating formula of the original PIO is redesigned. Then the stability of the modified PIO is discussed. Finally, the HPIO to generate the desired formation control in each time domain of the RHC is used. The simulation results verify the feasibility and effectiveness of the proposed method.

On the Turnpike Property and the Receding-Horizon Method for Linear-Quadratic Optimal Control Problems

Optimal control problems with a very large time horizon can be tackled with the receding-horizon control (RHC) method, which consists in solving a sequence of optimal control problems with small prediction horizon. The main result of this article is the proof of the exponential convergence (with respect to the prediction horizon) of the control generated by the RHC method toward the exact solution of the problem. The result is established for a class of infinite-dimensional linear-quadratic optimal control problems with time-independent dynamics and integral cost. Such problems satisfy the turnpike property: the optimal trajectory remains most of the time very close to the solution to the associated static optimization problem. Specific terminal cost functions, derived from the Lagrange multiplier associated with the static optimization problem, are employed in the implementation of the RHC method.

The effect of the terminal penalty in Receding Horizon Control for a class of stabilization problems

The Receding Horizon Control (RHC) strategy consists in replacing an infinite-horizon stabilization problem by a sequence of finite-horizon optimal control problems, which are numerically more tractable. The dynamic programming principle ensures that if the finite-horizon problems are formulated with the exact value function as a terminal penalty function, then the RHC method generates an optimal control. This article deals with the case where the terminal cost function is chosen as a cut-off Taylor approximation of the value function. The main result is an error rate estimate for the control generated by such a method, when compared with the optimal control. The obtained estimate is of the same order as the employed Taylor approximation and decreases at an exponential rate with respect to the prediction horizon. To illustrate the methodology, the article focuses on a class of bilinear optimal control problems in infinite-dimensional Hilbert spaces.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Optimal control of random evolutionary Boolean games

This paper studies the optimal control of n-person random evolutionary Boolean games (REBGs). Firstly, the considered n-person REBG is converted to an equivalent stochastic control system by using the semi-tensor product of matrices. Secondly, based on the equivalent stochastic control system, an algorithm is proposed to solve the finite horizon optimal control problem of n-person REBGs, which is applied to the Mayer-type optimal control problem. Thirdly, using the receding horizon control method, the infinite horizon optimal control problem is considered, and an algorithm is established. Finally, an illustrate example is given to support the obtained results.

Stochastic Receding Horizon Control of Active Distribution Networks With Distributed Renewables

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies.

Research on Methods of Active Steering Control Based on Receding Horizon Control

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.

Cloud computing-based energy optimization control framework for plug-in hybrid electric bus

Considering the complicated characteristics of traffic flow in city bus route and the nonlinear vehicle dynamics, optimal energy management integrated with clustering and recognition of driving conditions in plug-in hybrid electric bus is still a challenging problem. Motivated by this issue, this paper presents an innovative energy optimization control framework based on the cloud computing for plug-in hybrid electric bus. This framework, which includes offline part and online part, can realize the driving conditions clustering in offline part, and the energy management in online part. In offline part, utilizing the operating data transferred from a bus to the remote monitoring center, K-means algorithm is adopted to cluster the driving conditions, and then Markov probability transfer matrixes are generated to predict the possible operating demand of the bus driver. Next in online part, the current driving condition is real-time identified by a well-trained support vector machine, and Markov chains-based driving behaviors are accordingly selected. With the stochastic inputs, stochastic receding horizon control method is adopted to obtain the optimized energy management of hybrid powertrain. Simulations and hardware-in-loop test are carried out with the real-world city bus route, and the results show that the presented strategy could greatly improve the vehicle fuel economy, and as the traffic flow data feedback increases, the fuel consumption of every plug-in hybrid electric bus running in a specific bus route tends to be a stable minimum.

A matrix approach to modeling and optimization for dynamic games with random entrance

This paper investigates the algebraic formulation and optimization control for a class of dynamic games with random entrance by using the semi-tensor product method, and presents a number of new results. First, the given dynamic game is considered as a kind of networked evolutionary games with switch networks, based on which, it is formulated as a Markov processes to analyze. Second, using receding horizon control method, the given game’s optimization problem is solved by a state feedback controller, when the major player is considered as a control. Finally, an illustrative example is studied to support our new results.

Optimal reorientation of asymmetric underactuated spacecraft using differential flatness and receding horizon control

This paper presents a novel method integrating nominal trajectory optimization and tracking for the reorientation control of an underactuated spacecraft with only two available control torque inputs. By employing a pseudo input along the uncontrolled axis, the flatness property of a general underactuated spacecraft is extended explicitly, by which the reorientation trajectory optimization problem is formulated into the flat output space with all the differential constraints eliminated. Ultimately, the flat output optimization problem is transformed into a nonlinear programming problem via the Chebyshev pseudospectral method, which is improved by the conformal map and barycentric rational interpolation techniques to overcome the side effects of the differential matrix’s ill-conditions on numerical accuracy. Treating the trajectory tracking control as a state regulation problem, we develop a robust closed-loop tracking control law using the receding-horizon control method, and compute the feedback control at each control cycle rapidly via the differential transformation method. Numerical simulation results show that the proposed control scheme is feasible and effective for the reorientation maneuver.

Decentralized manufacturing management by a multi-agent optimal control method

A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results.

Optimal Control of Nanosatellite Fast Deorbit Using Electrodynamic Tether

This paper proposes a piecewise two-phased optimal control scheme for fast nanosatellite deorbit by a short electrodynamic tether. The first phase concerns the open-loop control trajectory optimization, where the optimal control problem is formulated only for the tether libration motion by assuming the slow-varying orbital elements of the electrodynamic tether system as constant within a discretized interval. The second phase deals with the closed-loop optimal control for tracking the derived optimal reference trajectory subject to multiple major orbital perturbations. The finite receding horizon control method is used in the optimal trajectory tracking. Both optimal control problems are solved by a direct collocation method based on the Hermite–Simpson method using discretization schemes with coincident nodes. The resulting nonlinear programming problem significantly reduces the problem size and improves the computational efficiency. Numerical results for fast nanosatellite deorbit by an electrodynamic tether in both equatorial and highly inclined orbits show the proposed method achieves high control accuracy and efficiency.

航空機の突風回避飛行に対する実時間最適制御

An onboard real-time optimal control method is applied to the guidance of an aircraft in the interest of balancing safe and efficient flight. The aircraft, which has a laser LIDAR (LIght Detection And Ranging) system to detect turbulence in the forward region, is given the real-time optimal control to avoid the region. The real-time optimal control algorithm in this study is based on Receding Horizon control method with Neighboring Extremal method. The Neighboring Extremal method, which solves the change in the optimal solution into the variation of the problem definition, can be applied only in the case that the variation is very small, and it is difficult to efficiently deal with inequality constraint conditions in the problem. This paper aims to resolve these issues. To confirm the effectiveness of the proposed algorithm, the avoidance flight from the turbulence that the aircraft encounters is numerically simulated, and satisfactory results are obtained.

Long term dynamics and optimal control of nano-satellite deorbit using a short electrodynamic tether

This paper studies the long term dynamics and optimal control of a nano-satellite deorbit by a short electrodynamic tether. The long term deorbit process is discretized into intervals and within each interval a two-phase optimal control law is proposed to achieve libration stability and fast deorbit simultaneously. The first-phase formulates an open-loop fast-deorbit control trajectory by a simplified model that assumes the slow-varying orbital elements of electrodynamic tethered system as constant and ignores perturbation forces other than the electrodynamic force. The second phase tracks the optimal trajectory derived in the first phase by a finite receding horizon control method while considering a full dynamic model of electrodynamic tether system. Both optimal control problems are solved by direct collocation method base on the Hermite–Simpson discretization schemes with coincident nodes. The resulting piecewise nonlinear programing problems in the sequential intervals reduces the problem size and improve the computational efficiency, which enable an on-orbit control application. Numerical results for deorbit control of a short electrodynamic tethered nano-satellite system in both equatorial and highly inclined orbits demonstrate the efficiency of the proposed control method. An optimal balance between the libration stability and a fast deorbit of satellite with minimum control efforts is achieved.

Receding Horizon Control on Automatic Landing Lateral Loop of Carrier-Based Aircraft

Since the angled deck is only tens miles width, the task of landing an aircraft on an aircraft carrier requires precise control, especially lateral loop. For this problem, this paper focuses on researching the aircraft automatic landing lateral control. In lateral control, the most crucial parts are controlling the off center distance and keeping the desired landing attitude. So firstly a nonlinear kinetic model of aircraft landing in lateral directional axis is established, and then transformed into error states. The controller is designed for an angle of attack of 11.7 deg and an airspeed of 40m/s, the equilibrium point. Receding horizon control methodology is employed to solve the aircraft lateral control problem. This controller is solved in MATLAB, and sent to the 3D simulation environment by network communication, to control the aircraft landing lateral loop. The simulation environment is programmed based on VC++ software. The simulation results show that receding horizon control method can achieve trajectory tracking and attitude tracking of nonlinear aircraft landing system.

Receding Horizon Control Method for Obstacle Avoidance System of Wheelchair with BMI

Receding Horizon Control Method for Obstacle Avoidance System of Wheelchair with BMI

Predictive Model for BOF Steelmaking Using RBF Neural Network

Effective control of the endpoint steel temperature and contents of carbon, sulphur, etc. is one of the main tasks of BOF steelmaking process. This paper established a multivariable predictive model for BOF steelmaking using RBF neural network. The input data is pretreated and standardized. Receding horizon control method is used to increase the accuracy of the model. Simulation and experiment comparisons show that the model is validated and has high hit rate.