Model Predictive Control Structure Research Articles

Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints

This paper proposes a neural network-based model predictive control (MPC) method for robotic manipulators with model uncertainty and input constraints. In the presented NN-based MPC structure, two groups of radial basis function neural networks (RBFNNs) are considered for online model estimation and effective optimization. The first group of RBFNNs is introduced as a predictive model for the robotic system with online learning strategies for handling the system uncertainty and improving the model estimation accuracy. The second one is developed for solving the optimization problem. By taking into account an actor–critic scheme with different weights and the same activation function, adaptive learning strategies are established for balancing between optimal tracking performance and predictive system stability. In addition, aiming at guaranteeing the input constraints, a nonquadratic cost function is adopted for the NN-based MPC. The ultimately uniformly boundedness (UUB) of all variables is verified through the Lyapunov approach. Simulation studies are conducted to explain the effectiveness of the proposed method.

Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches

In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

A novel fault-tolerant model predictive control (MPC)-based trajectory tracking approach for an aerial vehicle is presented in this study. A generalised online sequential extreme learning machine is introduced first to identify the corresponding coefficients of actuator faults. Subsequently, a robust trajectory tracking control is developed based on MPC, where the system constraints can be effectively considered in the designed control scheme. Trajectory tracking control is achieved by controlling only the acceleration of the aerial robot in the MPC structure. This leads to less computational burden and faster closed-loop dynamics. In addition, an effective disturbance observer is employed, which can satisfactorily capture the effects of unmodelled dynamics and external disturbances on the system dynamics. The stability of the proposed control algorithm is proved based on the Lyapunov theory under realistic assumptions. Finally, the proposed control strategy is applied to a quadrotor unmanned aerial vehicle. Both simulation and experimental results are presented to demonstrate the effectiveness of the introduced fault-tolerant control scheme. The obtained results suggest that the designed control approach is capable of stabilising the attitude and providing acceptable performance in tracking a predefined trajectory in a three-dimensional environment in the simultaneous presence of actuator faults and external disturbances.

Vehicle Yaw Stability Control at the Handling Limits Based on Model Predictive Control

This study proposes a double linear model predictive control (DLMPC) structure to improve the tyre force utilization of active front steering (AFS) vehicle at the handing limits. The DLMPC structure consists of a linear model predictive control (LMPC) and a linear time-varying model predictive control (LTV-MPC). During normal driving conditions, the AFS adopts LMPC to control the vehicle. When vehicle running at the handing limits, the tyre force is near saturation, LTV-MPC is activated to prevent tyre force from entering unstable region. The proposed DLMPC controller can reflect the nonlinear dynamic characteristic of tyres by the expression design of tyre force. Double lane change and sine with dwell maneuver tests are provided to demonstrate the effectiveness and feasibility of the DLMPC controller. Results corroborate that the proposed DLMPC controller can fully exploit the potential of AFS and maintain the yaw stability of the vehicle at the handling limits.

Improved model predictive control for the flow field in a wind tunnel with model uncertainty

This paper designs an improved model predictive control (MPC) strategy for the flow field in a transonic wind tunnel to quickly mitigate the overshoot and smooth the input and output signals under model uncertainty. A flow field control model is established first, followed by model discretization and the general MPC structure. Then, an MPC robust tuning approach is presented, which includes a bending temporal matrix design, and an adaptive robust temporal tuning algorithm. The bending temporal matrix is proposed to reduce the variability in the input profile in the temporal domain by penalizing high-frequency components, thus indirectly smoothing the input and output trajectories. The proposed adaptive robust temporal tuning algorithm can obtain an optimal tuning ratio by maximizing the control performance index of the system under model uncertainty. Simulation results show that the improved MPC controller yields better performance than a traditional MPC controller in mitigating the overshoot and smoothing the input profiles.

Fusion of inverse optimal and model predictive control strategies

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization

Abstract Establishing the balance between slurry supporting pressure and expected water-earth pressure is an important criterion to ensure excavating face stability in shield tunneling. To overcome the inaccuracy and hysteresis of manual operations, this paper presents a model predictive control (MPC) system for the slurry pressure balance during construction through effectively regulating the slurry circulation and air pressure holding systems according to geological conditions. The MPC structure consists of a diagonal recurrent neural network (DRNN) that approximates the complex relationship between slurry pressure and tunneling parameters, an optimizer which produces the optimal air pressure and slurry level based on the multi-step ahead predictions, and an evolved particle swarm optimization (EPSO) algorithm. The proposed EPSO can update the structure and weights of DRNN concurrently to better cater to the changeable stratum. The optimizer can excellently compensate the time delays in slurry pressure regulation by incorporating the logical control sequence of actuator systems into the EPSO procedure. The simulation results demonstrated that the presented approach can accurately track the desired water-earth pressure and significantly enhance the robustness of slurry supporting system in tunneling, and the novel EPSO also performed higher convergence speed and precision than the classic algorithms used for comparison.

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

SummaryThis research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.

Temperature setpoint-ramp control structure for batch reactors

Batch reactors can present challenging dynamic control problems because of the large nonlinear changes in process parameters during the batch cycle. Large reactant concentrations at the beginning of the batch produce fast reaction rates and large heat-transfer duties if the reactor temperature is increased too rapidly from its low initial temperature. Exothermic reactions can lead to reactor runaways that exceed maximum temperature/pressure safety constraints. Several papers have studied sophisticated techniques for detecting the potential of an impending runaway and taking corrective action. A complex stability criterion embedded in a model predictive control structure is a recent suggestion for handling the problem.This paper discusses a simple practical technique that has been used in industry for decades. The setpoint of a temperature/pressure controller is ramped up at a reasonable rate, but if the demand for coolant increases too much, the ramp is stopped until the demand for control effort drops down to a normal level.

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

A predictive control for autonomous vehicles using big data analysis

Abstract Big data analysis has an increasing importance in the field of the autonomous vehicles. It is related to vehicular networks and individual control. The paper proposes the improvement of a lateral autonomous vehicle control design through big data analysis on the measured signals. Based on the data a decision tree is generated by using the C4.5 and the MetaCost algorithms. It results in the regions of vehicle dynamic states and guarantees the tracking of the autonomous vehicle. The lateral control problem is formed in an MPC (Model Predictive Control) structure, in which the results of the big data analysis are built as constraints. The efficiency of the proposed method is illustrated through a comparative simulation example through a high-fidelity vehicle control software.

Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Considering some technical and economic reasons, it is not easy to directly measure the time-varying states which have a great impact on speed regulation performance in permanent magnet synchronous motor (PMSM) drive system. This paper proposes a method to estimate rotor speed, rotor position and load torque disturbance by utilising real-time estimations, based on the extended Kalman filter (EKF). In order to guarantee the accuracy and stability of PMSM drive system while running under load torque disturbance, this paper proposes a cascade closed-loop control strategy as a solution. The entire system is designed to track reference speed trajectory based on the estimated rotor speed, rotor position and load torque, using the model predictive control (MPC) algorithm based on linear PMSM model. Simulation results demonstrate the effectiveness of the states observer, and the cascade MPC structure shows the good performance in speed tracking of PMSM drive system.

A Combined MPC for Milling and Flotation – A Simulation Study

Abstract In the metals and mining industry, an ore-milling step followed by froth flotation is a very common processing route. It is also common that advanced controls of one form or another are installed on these units. It is less common for the advanced controllers to be combined in any formal way. This paper explores the way two typical model predictive control structures could be combined, and what are the benefits of doing so. Use is made of existing linear dynamic models of plants the authors have implemented model predictive control (MPC) on. The results show that the coordinated system can behave differently to the separate controllers, depending on the steady-state optimisation coefficients applied.

Inferring Control Intent during Seated Balance using Inverse Model Predictive Control.

Patients with Low Back Pain (LBP) are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Model Predictive Control (iMPC) problem. The standard Model Predictive Control (MPC) structure includes constraints, therefore, it allows us to model the physiological constraints of motor control. We devised an iMPC algorithm to solve iMPC problems with experimentally collected output trajectories. We used experimental data of one healthy subject during a seated balance test that used a physical Human-Robot Interaction (pHRI). Results show that the estimated MPC weights reflected the task instructions given to the subject and yielded an acceptable goodness of fit. The iMPC solution suggests that the subject's control intent was dominated by minimizing the squared sum of a combination of the upper-body and lower-body angles and velocities.

Ecological and safe driving: A model predictive control approach considering spatial and temporal constraints

Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin’s maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system.

Model predictive rollover prevention for steer-by-wire vehicles with a new rollover index

ABSTRACTRecent advances in active safety systems provide new opportunities to handle rollover problems more efficiently. This paper presents a Model Predictive Control (MPC) strategy for rollover prevention through active front steering. A new Rollover Index (RI) and a specified vehicle model are proposed to realise an effective rollover prevention approach. The new RI is represented by vehicle parameters and state variables so that it can be readily incorporated into the MPC objective function. Using a barrier cost function, the proposed MPC controller limits vehicle maneuvers within a safe region of rollover without sacrificing vehicle maneuverability under normal conditions. The roll dynamics, the tire nonlinearity, the lateral load transfer, and the road bank are considered for vehicle modelling to provide appropriate actions at the threshold of a rollover. An affine tire model is used to handle tire nonlinearity in the MPC structure. A high-fidelity CarSim model for an SUV is employed to evaluate the proposed RI and the MPC controller. Simulation results show the reliability of the proposed RI and the effectiveness of the proposed rollover prevention controller for different rollover situations.

Variable horizon MPC for energy management on dual planetary gear hybrid electric vehicle

This paper aims at studying the energy management for the dual planetary gear hybrid electric vehicle based on the model predictive control structure with the variable horizon velocity prediction. According the identification of characteristic parameters of historical velocity, the optimal predictive horizon is obtained by considering the energy consumption. Different deep neural networks are trained and applied to predict the future variable horizon velocity through the prediction accuracy. The simulation results show that the proposed method can achieve a 1.6% reduction of the energy consumption and a 57.8% computing time saving compared with the model predictive control in a fixed horizon.

Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming.

This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method.

Control System Design for Visual Positioning of a Ship Based on NMPC and Multi-objective Structure

Abstract The paper is devoted to the area of visual feedback control and the problem of visual dynamic positioning of a ship is considered. It is assumed that the ship is equipped with a camera, through which the necessary information about the objects in 3D world is extracted. The control objective is to provide a given projection of some observable 3D object onto the image plane of the camera. The important feature of the system is that the mathematical model describing its dynamics is essentially nonlinear. The proposed approach to the control system design is based on the nonlinear model predictive control (NMPC) and multi-objective structure of the control law. NMPC is used to calculate the optimal nominal motion on the prediction horizon and the multi-objective structure is applied to design a stabilizing control law in the vicinity of this motion. The obtained results are illustrated by practical example.

Model based predictive engine torque control for improved drivability

Elasticity of the various driveline components and backlash originating from gear reduction mechanisms and fasteners may cause torsional vibrations resulting in unintended shunt and shuffle behaviours, when a vehicle is subjected to an acceleration change request. As a result of recent improvements to engine control structures and computational capability developments during the last few decades, the idea of using generated brake torque control has been considered a state of the art research topic among academic researchers and original equipment manufacturers (OEMs). In order to improve transient vehicle response to an acceleration change manoeuvre, a novel engine generated brake torque based model predictive control (MPC) algorithm with an additional anti-shuffle control element has been developed to manipulate the pedal map oriented torque demand signal in an automotive powertrain application. A four mass powertrain model was built and model validation considering longitudinal vehicle dynamics was performed with vehicle level tests using a tip-in followed by a tip out acceleration pedal signal input manoeuvre. Comparison of simulation results and vehicle test data shows that the proposed model is capable of capturing the vehicle acceleration profile revealing unintended error states for the specified input signals. MPC structures based on three mass vehicle models (derived from the four mass model via subtracting tyre dynamics due to high order nonlinearities at the tyre model) was developed in a “MATLAB/Simulink” environment to obtain a smooth and responsive acceleration profile. The MPC controller delivers signal states with the expected performance metrics without error states such as excessive jerks and shuffles. An additional engine to wheel speed difference based proportional controller is employed in order to further reduce powertrain oscillations without compromising from overall system response speed resulting in a comfortable drive.