Receding Horizon Predictive Control Research Articles

A Health-Oriented Power Control Strategy of Direct Drive Wind Turbine

Offshore wind power has great development potential, of which operation and maintenance costs account for a large proportion in the full lifecycle cost. Power output control strategy plays a key role in wind turbine generator system (WTGS). Traditional control strategies, such as maximum power point tracking and unity power factor, which only uses current local information for decision-making, are not the optimal control method when considering the component fatigue. For the reason, this paper proposes a health-oriented power control strategy, which transforms control problem into an equivalent solution to an economic revenue model. The model aimed to maximize the economic revenue of the full lifecycle, and employed physics-of-failure methodology to quantify the remaining useful life, involving detailed mission profiles. Further, a receding horizon predictive controller is designed to flatten the junction temperature fluctuation and extend converter lifespan, for engineering application. A 1.2 MW WTGS is utilized as a benchmark to verify the proposed strategy performance, and the numerical results reveal the mechanism of the optimized control strategies for alleviating the fatigue and aging of the converters.

Distributed Cooperative Search Algorithm With Task Assignment and Receding Horizon Predictive Control for Multiple Unmanned Aerial Vehicles

For target search using multiple unmanned aerial vehicles (UAVs) while knowing the probability distribution of the targets, a distributed cooperative search algorithm aiming to minimize the search time is proposed. First, an importance function for the representation of the environment is designed. Second, a mission planning system (MPS) is proposed, consisting of preliminary planning, task assignment, and post-planning layers. In the MPS, the search region is divided into a series of sub-regions of different sizes by centroidal Voronoi tessellation; these are regarded as subtasks assigned to the UAVs. The loading of the MPS improves the performance of global planning of the UAVs. Finally, receding horizon predictive control is used to plan the paths of the UAVs online. Moreover, the conflict between the requirements of target search and connectivity maintenance of the UAVs is mitigated using the minimum spanning tree strategy to optimize the communication topology while considering the communication cost when evaluating the tasks. The results of Monte Carlo simulations show that the introduction of the MPS into the traditional cooperative search framework effectively improves search and coverage efficiency.

Prevention of Load Encroachment Using Model Predictive Control

Tripping of distance relays due to load encroachment is one of the common causes behind major disturbances occur in power systems. Several techniques have been presented in literature to differentiate an occurrence of load encroachment and a fault in a system. However, once an event of load encroachment is observed, loading condition of the power system must be rescheduled to avoid any undesired relay tripping. This paper proposes a receding-horizon Model Predictive Control (MPC) strategy to change load and generation of a power system optimally to take the apparent impedance seen by vulnerable distance relays away from their zone-3 boundaries. To apply MPC technique, a linear state-space model of power system is developed using sensitivity analysis. Rescheduled load and generation are found by minimizing the total cost required for the change, owing to the constraints of power balance, power carrying capacity of lines and relay operational margins. During step changes in inputs, adequate time gap is provided to ensure the stability of the power system. An oscillation detection technique is developed for the above purpose. Several case studies have been presented at the end to demonstrate the performance of the proposed technique under stressed operating conditions.

Robust model predictive control for a small reverse osmosis desalination unit subject to uncertainty and actuator fault

Abstract Actuator faults are inevitable in small reverse osmosis desalination plants. They may cause energy losses and reduce the quality of the freshwater, which may endanger human life. Model predictive control (MPC) is a model-based approach widely used to control process systems such as reverse osmosis, while considering a set of constraints. In this paper, three methods of predictive model controllers are considered for the control of a multi-input multi-output (MIMO) reverse osmosis desalination system in the presence of noise, model mismatch, and actuator fault. Formulation of enhanced constrained receding horizon predictive control via bounded data uncertainties (CRHPC-BDU) are extended for linear time-invariant MIMO systems. Permeate flow rate and conductivity of the water produced are controlled by a retentate valve and a bypass valve, respectively. The simulation results show the robustness of the suggested approach in the presence of both noise and uncertainties. CRHPC-BDU has a better performance subject to systems with model uncertainty and actuator fault up to a reasonable limit. By increasing the actuator fault up to 34%, the robustness of CRHPC-BDU is further highlighted in permeate conductivity, where the fluctuations of permeate conductivity dampen sooner than in the other two controllers.

Switching Multi-Objective Receding Horizon Control for CACC of Mixed Vehicle Strings

Vehicle safety, ride comfort, formation control and fuel economy are the main control objectives of cooperative adaptive cruise control (CACC) of vehicle strings. This paper considers the multi-objective CACC problem of a mixed vehicle string, i.e., the vehicle string is composed of CACC-, ACC- and human-driven vehicles. A new switching multi-objective receding horizon predictive control method is proposed for CACC of mixed vehicle strings. The longitudinal models of ACC- and human-driven vehicles are used to estimate the driving behaviors of the individual vehicles. Then three multi-objective predictive controllers are separately designed for different vehicle scenarios. According to different preceding vehicles, the multi-objective predictive controllers are switched online in the normal situation, and in the dangerous situation, the predictive controller is switched to the safety controller to ensure driving safety of vehicles in the string. To verify the effectiveness of the proposed control method, a mixed vehicle string consisting of six cars is used in the simulation experiment with complex traffic scenarios.

Investigation of MPC-based Engine Torque Prediction Control Strategy for a Parallel Hybrid Electric Vehicle

To improve the fuel economy of a parallel hybrid power vehicle, the torque distribution strategy based on engine prediction is investigated. The proposed approach combines the rule logic and optimization algorithm. The 'state-of-charge’ and acceleration rate are selected as the control parameters for the mode transition.When the system is working in the torque-split mode, the model predictive control (MPC) of the optimization algorithm is implemented to establish the receding horizon predictive control optimization strategy. The fuel consumption is defined as the target cost function and the engine torque serves as the control variable. The orthogonal simulation experiment is carried out to examine the two MPC parameters, which are the prediction horizon and weight coefficient. Specifically, the prediction time domain variable tradeoff factor was determined. The prediction time domain <italic>S</italic> was chosen as 3 <italic>s</italic>, and the tradeoff factors were 0.0001, 0.00001, and 0.000001, respectively.In the prediction time domain of constant tradeoff variable, the tradeoff factor is 0.0001, and the prediction time domain <italic>S</italic> are 3 <italic>s</italic>, 5 <italic>s</italic>, and 10 <italic>s</italic> respectively. The results indicate that the best solution is the group consisting of the prediction horizon <italic>S</italic> = 3 <italic>s</italic> and weight coefficient <italic>λ</italic> = 0.0001; the equivalent integrated fuel consumption for 100 km is 6.179 L/100 km. In addition, comparison of the proposed strategy and the rule-based strategy showed that the former increased fuel economy by 7.8%. Through the above comparative experiments, the effectiveness of the MPC algorithm for improving fuel economy is further demonstrated.

RBF-ARX model-based MPC strategies with application to a water tank system

A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF) networks and linear ARX model structure is utilized for representing the dynamic behavior of a class of smooth nonlinear and non-stationary systems. This model is locally linear at each working point and globally nonlinear within whole working range. Based on the structural characteristics of the RBF-ARX model, three receding horizon predictive control (RBF-ARX-MPC) strategies are designed: (1) the RBF-ARX-MPC algorithm based on single-point linearization (MPC-SPL); (2) the RBF-ARX-MPC algorithm based on multi-point linearization (MPC-MPL); and (3) the RBF-ARX-MPC algorithm based on globally nonlinear optimization (MPC-GNO). In the MPC-SPL, the future multi-step-ahead predictive output of the system is obtained based on the local linearization of the RBF-ARX model at only current working-point, while in the MPC-MPL the future long-term output prediction is obtained according to the future local characteristics from previous online optimization results of the RBF-ARX model based MPC. In the MPC-GNO, the globally nonlinear characteristics of the RBF-ARX model are fully used for online getting control variables of the MPC. Real-time control experiments for the three type MPCs are carried out on a water tank system, which are also compared with a classical PID control and a traditional linear ARX model-based MPC. The results verify that the modeling method and the model-based predictive control strategies are realizable and effective for the nonlinear and unstable system. Moreover, it is also shown that the MPC-GNO can obtain better control performance but need more computation time compared to the other MPCs, which makes it possible to be applied into some slowly varying processes.

Analysis of turbulent flow properties and energy fluxes in optimally controlled wind-farm boundary layers

In the present work our focus is to improve the performance of a wind farm by coordinated control of all turbines with the aim to increase the overall energy extraction by the farm. To this end, we couple flow simulations performed using Large Eddy Simulations (LES) with gradient based optimization to control individual turbines in a farm. The control parameters are the disk-based thrust coefficient of individual turbines as a function of time. They indirectly represent the effect of control actions that would correspond to blade-pitching of the turbines. We employ a receding-horizon predictive control setting and solve the optimization problem iteratively at each time horizon based on the gradient information obtained from the evolution of the flow field and the adjoint computation. We find that the extracted farm power increases by approximately 16% for a cost functional that is based on total energy extraction. However, this energy is gained from a slow deceleration of the boundary layer which is sustained for approximately 1 hour. We further analyze the turbulent stresses and compare to wind farms without optimal control.

Robust lap-time simulation

Lap-time simulation is an important computational tool for many motor racing teams, but the assumption of ideal operating conditions and a ‘perfect’ racing driver is usually made. The aim of this paper is to extend lap-time simulation to include robustness to disturbances on the vehicle and robustness to mistakes by the driver. A previously developed receding-horizon model predictive control with a convex optimisation method for lap-time simulation is extended with a tube-based technique for robust model predictive control. A linear quadratic regulator compensatory controller maintains the vehicle close to the nominal vehicle path and speed trajectories in the presence of disturbances to the vehicle. An ensemble of disturbed trajectories defines a tube of trajectories, which is then used to modify the nominal vehicle path so that the track boundary constraints are satisfied in the presence of the disturbances. The lap time versus r.m.s. steering velocity curves for two different vehicle models demonstrate the expected trade-off: a decrease in the activity of the compensatory steering control increases the tube width, which leads to a longer lap time. The technique has application to designing and setting up cars to perform well in the presence of disturbances.

Quad‐Rotor Modeling and Attitude Control Using State‐Dependent ARX Type Model

AbstractA state‐dependent autoregressive with exogenous variables (SD‐ARX) model whose functional coefficients are approximated by sets of radial basis function (RBF) networks is proposed to describe the dynamic behavior of a quad‐rotor in this paper. This model is identified offline and used as an internal predictor of a receding horizon predictive controller to address the quad‐rotor's attitude control issue. In addition, the physical constraints of the system have been also taken into account during the controller design process. The results of real‐time control on a quad‐rotor aircraft illustrate satisfactory modeling accuracy in a large operating range and good performance of control approach proposed in this paper.

Mejora de la estabilidad, robustez y tiempo de cómputo en CRHPC con BDU

In this work, different control laws of Model Predictive Control (MPC) are offered in order to improve the stability and robustness aspects. The CRHPC (Constrained Receding-Horizon Predictive Control) provides de stability (in the absence of uncertainty), and the BDU (Bounded Data Uncertainties) technique provides a guided regularization improving the robustness and ill-conditioning, presenting the original CRHPC-BDU. The objective of this work consists, on the one hand, of offering alternative control laws of the CRHPC, using different mathematical tools and comparing them. These ones constitute the basis for the different statements for extending these strategies to the CRHPC-BDU. So, one of the main contribution consists of obtaining a more robust and with less computationalcost version of CRHPC-BDU.

Design and comparison of autopilots of an air-to-surface antitank missile and its terminal guidance study

The design of the autopilot is one of the most important algorithms of missiles. Performance of the autopilot and its robustness are significant matters to hit a target accurately. The autopilot should satisfy the desired performance under disturbances. In the scope of this study, three autopilots were offered for tracking pitch acceleration command using different control methods: three-loop classic control, pole-placement control and receding horizon predictive control. The aim of the autopilot designed by employing receding horizon predictive control is to minimize the flight control effort, and to make the close-loop system insensitive against modelling uncertainties and stochastic shattering factors. This study comes up with that the missile is able to move in desired performance under disturbances such as control surface misplacement, thrust misalignment, wind and aerodynamic uncertainties with more robustness, less control effort and minimum miss distance and terminal time using an alternative control method instead of classic and pole-placement control methods which are generally referred by the defence industry.

Computations of optimal cylinder flow control in weakly conductive fluids

A nonlinear adjoint-based optimal control approach of cylinder wake by electromagnetic force has been investigated numerically in the paper. A cost functional representing the balance of the regulated quantities with different weights and interaction parameter N (Lorentz force) has been constituted, where the regulated quantities related with flow and force are taken as targets of regulation and the Lorentz force, (as interaction parameter N), is taken as a control input. Based on the cost functional and Navier–Stokes equations, the corresponding adjoint equations have been derived and the sensitivity of the cost functional is found to be a simple function of the adjoint stream function in the adjoint field. For the different regulations, the forms of optimal control rules are similar while the adjoint equations are different. The receding-horizon predictive control setting is employed to discuss the optimal control problems. Under the action of optimal N( t), the flow separation is suppressed fully, so that the oscillations of drag and lift are suppressed and the total drag coefficient decreases dramatically. For the different regulations, the control effects have some differences due to the different values of optimal inputs corresponding to the different adjoint flow fields.

Robust constrained receding-horizon predictive control via bounded data uncertainties

The main objective of this work consists of obtaining a new robust and stable Model Predictive Control (MPC). One widely used technique for improving robustness in MPC consists of the Min–Max optimization, where an analogy can be established with the Bounded Data Uncertainties (BDU) method. The BDU is a regularization technique for least-squares problems by taking into account the uncertainty bounds. So BDU both improves robustness in MPC and offers a guided way of tuning the empirically tuned penalization parameter for the control effort in MPC due to the duality that the parameter coincides with the regularization one in BDU. On the other hand, the stability objective is achieved by the use of terminal constraints, in particular the Constrained Receding-Horizon Predictive Control (CRHPC) algorithm, so the original CRHPC–BDU controller is stated, which presents a better performance from the point of view of robustness and stability than a standard MPC.

Robust and stable predictive control with bounded uncertainties

Min–Max optimization is often used for improving robustness in Model Predictive Control (MPC). An analogy to this optimization could be the BDU (Bounded Data Uncertainties) method, which is a regularization technique for least-squares problems that takes into account the uncertainty bounds. Stability of MPC can be achieved by using terminal constraints, such as in the CRHPC (Constrained Receding-Horizon Predictive Control) algorithm. By combining both BDU and CRHPC methods, a robust and stable MPC is obtained, which is the aim of this work. BDU also offers a guided method of tuning the empirically tuned penalization parameter for the control effort in MPC.

RBF-ARX model-based nonlinear system modeling and predictive control with application to a NO x decomposition process

This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBF-ARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending on the working-points of a system. The RBF-ARX model is constructed as a global model, and is estimated off-line so as to avoid the possible failure of on-line parameter estimation during real-time control. A receding horizon predictive control (RBF-ARX-MPC) strategy based on the RBF-ARX model that does not require on-line parameter estimation for the nonlinear system is presented. The local linearization of the system at each working-point may be easily obtained from the global RBF-ARX model and so the use of nonlinear programming techniques to solve the on-line optimization problem with constraints in RBF-ARX-MPC is also avoided. A fast-converging estimation method is applied to optimize the RBF-ARX model parameters. A case study and example of an industrial experiment on the nitrogen oxide (NO x ) decomposition process in thermal power plants are given to demonstrate the modeling precision and control performance.

복소형 다각형 불변영역을 이용한 입력제한 예측제어

The concept of feasible & invariant region plays an important role to derive closed loop stability and achie adequate performance of constrained receding horizon predictive control. In this paper, we define a complex polyhedral feasible & invariant set for all stabilizable input-constrained linear systems by using a complex transform and propose a one-norm based receding horizon control scheme using these invariant sets. In order to get a larger stabilizable set, a convex hull of invariant sets which are defined for different state feedback gains is used as a target invariant set of the constrained receding horizon control. The proposed constrained receding horizon control scheme is formulated so that it can be solved via linear programming.

Constrained receding horizon predictive control for nonlinear systems

The paper concerns the receding horizon predictive control of constrained nonlinear systems and presents an algorithm which relies on the online solution of a simple linear program (LP). Use is made of a finite control horizon in conjunction with a terminal inequality constraint and a predicted cost that includes a terminal penalty term. The optimization procedure is based on predictions made by linearized incremental models at points of a given seed trajectory and the effects of linearization error are taken into account to give a bound on the predicted tracking error. The algorithm is posed in the form of an LP and the proper selection of the terminal penalty term of the predicted cost guarantees the asymptotic stability. The results of the paper are illustrated by means of a simple example.

Receding horizon predictive control for nonlinear time-delay systems with and without input constraints

This paper proposes a receding horizon predictive control (RHPC) for nonlinear time-delay systems. The control law is obtained by minimizing finite horizon cost with a terminal weighting functional. An inequality condition on the terminal weighting functional is presented, under which the closed-loop stability of RHPC is guaranteed. It is also shown that the stability can be guaranteed for input-constrained systems under an additional functional inequality constraint on the terminal state trajectory. A special class of nonlinear timedelay systems is introduced and a systematic method to find a terminal weighting functional satisfying the proposed inequality condition is given for these systems. Through a simulation example, it is demonstrated that the proposed RHPC has the guaranteed closed-loop stability for nonlinear time-delay systems with and without input constraints.

Autonomous Agents for Traffic Simulation and Control

The development of an infrastructure for multiple autonomous agents, with an application to urban traffic signal control, is described. The agent-based infrastructure, Cybele, allows for distributed computing, interagent communication, agent migration, and computational resource allocation. The agents that are used to solve the traffic signal control problem are known collectively as DAARTS (Decentralized Adaptive Agents for contRol of Traffic Signals). DAARTS adopts a hierarchical multiagent-based architecture in which the lowest level (intersection agents) involves individual intersection-traffic dynamics and phase selection based on “local” information, while higher levels take into account the supervisory (network-level) dynamics. The controller design is based on a receding-horizon model predictive control approach. Coordination between intersections is achieved in a decentralized manner at the lowest level. The agents are integrated into a simulation test bed with the microsimulator CORSIM, using the DAARTS simulation tool kit. This kit enables communications between the CORSIM real-time extension, the communications management functions of Cybele, and the traffic control agents. The control process is truly distributed, and each of these components can reside on a different computer. Descriptions of all of the software components are given, and the control algorithm is discussed in detail. Some encouraging results from the simulation of a small network are included. Ongoing and future research activities are discussed.