Model Predictive Control Method Research Articles

Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Development of data-based model predictive control for continuous damping and air spring suspension system

This paper presents the development of a data-based model predictive control method for a semi-active suspension system with air springs and continuous damping. A continuous damping controller (CDC) has been devised for the system to alter its damping coefficient in real-time, capable of reducing the impact of external road disturbances. In this research, the damping force has been split up into a nominal force and a controllable additional force, allowing the system to be modelled as a linear time-invariant system, despite the inherent nonlinearity of air spring suspension systems. In consideration of such constraints given by the damper, a model predictive controller (MPC) has been devised with the goal of improving ride comfort. Additionally, Gaussian process regression (GPR) has been used to compensate for output estimation errors arising from model parameter uncertainties. The semi-active suspension system also featured a multichamber air spring with three available modes of stiffness. Hence, a stiffness controller has been designed to select an appropriate mode based on the predicted vehicle states given by the MPC using road preview information and a reduced full-car model. The proposed algorithm has been verified using computer simulations. The results showed that compensations for model errors made with GPR significantly improves ride comfort even in the presence of parameter uncertainties. Additionally, both the damping controller and stiffness mode selector were successfully implemented into an actual test vehicle. Vehicle test results showed the proposed algorithm to be robust and effective in enhancing ride comfort and reducing vehicle pitch motion.

Improved model predictive control method for a five-level inverter with coupled inductors

Improved model predictive control method for a five-level inverter with coupled inductors

Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control

Advanced energy management strategy (EMS) can ensure healthy, stable, and efficient operation of the on-board energy systems. Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) are two powerful control methods that have been extensively researched in the field of vehicle energy management. However, there are some problems with both approaches. On the one hand, MPC is difficult to cope with the complex systems and the excessive computational load caused by the non-linear solving over long prediction horizon, on the other hand, DRL lacks adaptability to different driving conditions and is poorly interpretable. Therefore, this paper innovatively proposes a learning-based model predictive (L-MPC) EMS for fuel cell hybrid electric bus (FCHEB) with health-aware control. This method effectively merges the advantages of control theory and machine learning. Specifically, firstly, the precise aging models for vehicular energy systems are established and incorporated into the optimization framework along with hydrogen consumption related metrics. Secondly, the principles of the learning-based MPC algorithm are thoroughly elucidated. In addition, to ensure driving details under future conditions, a speed predictor based on a double-layer Bi-directional Long Short-Term Memory (BiLSTM) is proposed at the strategy supervision layer. Finally, the superiority of the proposed strategy in prolonging the lifespan of the energy systems and reducing overall vehicle operating cost is verified by comprehensive comparisons with state-of-the-art MPC and DRL methods under real-world collected driving condition.

A real‐time critical‐scenario‐generation framework for defect detection of autonomous driving system

AbstractTo find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors’ approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations.

Enhanced Dual–Vector Model Predictive Control for PMSM Drives Using the Optimal Vector Selection Principle

The Dual–Vector model predictive control (DV–MPC) method can improve the steady–state control performance of motor drives compared to using the single–vector method in one switching cycle. However, this performance enhancement generally increases the computational burden due to the exponential increase in the number of vector selections, lowering the system’s dynamic response. Alternatively, limiting the vector combinations will sacrifice system steady–state performance. To address this issue, this paper proposes an enhanced DV–MPC method that can determine the optimal vector combinations along with their duration time within minimized calculation times. Compared to the existing DV–MPC methods, the proposed enhanced technique can achieve excellent steady–state performance while maintaining a low computational burden. These benefits have been demonstrated in the results from a 2.5k rpm permanent magnet synchronous motor drive.

Improved multistep model predictive control method with reduced finite set for MMC-UPQC

Multistep model predictive control (M-MPC) can improve control accuracy but leads to an increased computational burden. To reduce the computational burden and restore power quality, an improved M-MPC method with reduced finite set for unified power quality conditioner (UPQC) based on modular multilevel converter (MMC) is presented in this paper. First, a finite set of M-MPC is formed according to the inserted sub-modules (SMs) number calculated from cost functions. Second, we investigate the relationship between the inserted SMs number and the bridge arm output voltages and find that the inserted SMs number will increase as voltages rise. As such, by judging the change rate of the output voltages, switching states that cannot be realized within the finite set can be eliminated, thus the finite set can be reduced. In addition, circulating currents and capacitance voltage control methods are designed to ensure the internal stability of MMC. Finally, the MATLAB/Simulink simulation and the real time laboratory (RT-LAB) based hardware-in-the-loop (HIL) experimental results show effectiveness of the proposed M-MPC. The results show the proposed M-MPC exhibits better dynamic characteristics and fewer control options as compared to the single-step MPC.

Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

A dual-loop feedback trajectory tracking control for rock drilling hydraulically driven manipulator

This paper is devoted to proposing a feedback trajectory tracking control method for hydraulically driven rock drilling robotic manipulators. The rock drilling robot has a large-scale long boom and multiple joints (6R-2P), so the deformation of the arm may not be neglected. A kinematic model with the flexible deformation of the boom is developed by adding a modified coordinate transformation matrix to the general model. Each joint motion is determined by solving the inverse kinematic model in an optimization algorithm, which aims at minimizing the positional error and smoothing the motion. A dual-loop feedback controller is proposed to track the end-effector’s position and orientation. Based on the model predictive control (MPC) method, the inner-loop controller controls the cylinder motion by controlling the cylinder motion. The outer-loop controller uses the fuzzy-PID control to correct the position error which is caused by the inner-loop controller. Several reference trajectories are designed to validate the effectiveness of the proposed dual-loop control strategy, which demonstrates it has higher accuracy and lower control fluctuation than single-loop and other dual-loop controllers.

Reinforcement Learning versus Model Predictive Control on greenhouse climate control

The greenhouse system plays a crucial role to ensure an adequate supply of fresh food for the growing global population. However, maintaining an optimal growing climate within a greenhouse requires resources and operational costs. To achieve economical and sustainable crop growth, efficient climate control in greenhouse production is paramount. Model Predictive Control (MPC) and Reinforcement Learning (RL) are the two approaches representing model-based and learning-based control, respectively. Each one has its own way to formulate control problems, define control objectives, and seek for optimal control actions that provide sustainable crop growth. Although certain forms of MPC and RL have been applied to greenhouse climate control, limited research has comprehensively analyzed the connections, differences, advantages, and disadvantages between these two approaches, both mathematically and in terms of performance. Therefore, this paper aims to address this gap by: (1) introducing a novel RL approach that utilizes Deep Deterministic Policy Gradient (DDPG) for large and continuous state–action space environments; (2) formulating the MPC and RL approaches for greenhouse climate control within a unified framework; (3) exploring the mathematical connections and differences between MPC and RL; (4) conducting a simulation study to analyze and compare the performance of MPC and RL; (5) presenting and interpreting the comparative results to provide valuable insights for the application of these control approaches in different scenarios. By undertaking these objectives, this paper seeks to contribute to the understanding and advancement of both MPC and RL methods in greenhouse climate control, fostering more informed decision-making regarding their selection and implementation based on specific requirements and constraints.

LSTM-MPC: A Deep Learning Based Predictive Control Method for Multimode Process Control

Modern industrial processes often operate under different modes, which brings challenges to model predictive control (MPC). Recently, most MPC related methods would establish prediction models independently for different modes, which results in their control effect highly relying on switching strategies. Inspired by the powerful representation capabilities of deep learning, this paper proposed a deep learning based MPC method. Specifically, the LSTM network is applied to predict behaviours of controlled system, which can automatically match different operation modes without switching strategy. Then combined with MPC framework, an adaptive gradient descent method is introduced to handle optimization problem and its constraints. In addition, stability and feasibility analysis have been conducted from the aspect of theory to ensure practical application of the proposed method. Experiments on a numerical simulation process and an industrial process platform show the strength and reliability of the proposed method, which reduces the overshoot by about 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared to common learning-based MPC methods and improves the control accuracy effectively.

A Differential Error Based Self-Triggered MPC With Adaptive Prediction Horizon For Discrete Systems

Abstract For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework.

Reinforcement learning approach for optimal control of ice-based thermal energy storage (TES) systems in commercial buildings

Ice-based thermal energy storage (TES) system is effective on load shifting and demand response in public buildings under time-of-use (TOU) tariffs. The management and allocation of ice storage and release during the day are vital to cost efficiency and energy performance of the TES system. Currently, fixed-schedule, rule-based, and model predictive control methods are widely used in cost-saving control of ice-based TES system, but may not resolve the issue under detailed virtual environment reflecting cooling load uncertainty and system performance complexity. This study proposes a reinforcement learning (RL) approach for optimal control of ice-based TES systems in commercial buildings. The RL framework is defined according to the TOU tariff, predicted cooling load, and simulated cooling plant performance. Deep Q-network RL agent is tuned and trained for control decision-making with multi-step temporal difference and ε-greedy algorithm. A detailed environment model with heat transfer simulation is developed to train and evaluate the RL agent. The environment model is calibrated with the measured data from a case study of an ice-based TES system. Compared to the fixed-schedule strategy, the proposed RL controller achieves 7.6% of cost reduction in the simulated cooling season of 2020. The RL-based control approach can effectively learn the features and improve the cost efficiency of the ice-based TES system.

Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller.

Hierarchical distributed MPC method for hybrid energy management: A case study of ship with variable operating conditions

The energy management (EM) strategy, power controller, and local controller of the EM system are coupled, and together affect hybrid power system performance. To achieve coordinated control and multi-objective optimisation of a hybrid power system, a hierarchical distributed control method was proposed, and a validation study was conducted based on model-in-the-loop simulations (MILs) and hardware-in-the-loop simulations (HILs). First, a real-time EM strategy based on a combination of forward dynamic programming (FDP) and model predictive control (MPC) methods was proposed to optimise the power allocation for multiple energy sources. Second, a distributed controller based on a consistency algorithm was proposed to coordinate the differences in the characteristics of multiple energy sources. A dynamic virtual impedance-based droop controller was designed for dynamic tracking of the reference power. The performance of the three-layer control structure was comprehensively validated through MILs and HILs. The results show that the dynamic virtual impedance-based droop control method can track the nonlinear reference power with control deviation within 5 %. The distributed controller is able to keep the bus voltage stable. The global control accuracy of the DC bus voltage is improved by 18.08 %, and the battery current global fluctuations is reduced by 17.4 %. The real-time FDP-MPC-based EM strategy can achieve 99.89 % energy savings and emissions reduction compared to the global DP-MPC-based method, with greater robustness, real-time performance. The results demonstrate that the proposed method is effective for real-time applications and can achieve hierarchical and multi-objective optimal control of the hybrid power system.

Constrained model predictive control for 3-D offshore boom cranes

Offshore boom cranes are complex nonlinear underactuated systems, whose control problems, when considering the 3-dimensional (3-D) model affected by ship-induced disturbances, are full of various challenges. In fact, it still remains an open problem to efficiently control the 3-D offshore boom cranes. Moreover, most existing works on offshore cranes concentrate upon designing the force/torque controller, whose performance degrades badly in the presence of friction. In this paper, considering the above issues, a novel model predictive control (MPC) method, which successfully considers the constraints of both input and output of the system, is proposed to achieve satisfactory control performance even under the effect of persistent ship roll and heave perturbations. Specifically, a discrete model is first obtained by some careful transformation and discretization on the unactuated dynamics equations, based on which a novel model predictive controller is constructed. To reduce the complexity of the system, the unactuated states and the accelerations of actuated states are considered as system states and control inputs respectively. After that, the requirements for the payload position accuracy and swing suppression as well as other system constraints, are taken into full account by converting them into input constraints to facilitate subsequent handling. At last, hardware experiments are implemented on a self-built testbed, with the obtained results clearly illustrating the effectiveness and robustness of the proposed method.

An improved model predictive control method for path tracking of autonomous vehicle considering longitudinal velocity

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature.

Safety MPC of constrained switched systems with control barrier function

This paper studies model predictive control (MPC) with control barrier function to solve the stability and safety problems of switched systems subject to input and state constraints. Considering that the system state is unmeasurable and disturbed, an observer-based MPC H ∞ method is adopted to estimate the system state and suppress disturbances. A cost function is designed to optimise the state and control input of switched systems by minimising its upper bound, and the optimisation problem is further transformed into a feasibility problem. Moreover, a control barrier function is introduced to constrain the controller gains, which ensures that the initial state of switched systems converges to the steady state without entering the specified unsafe region. Furthermore, a set of matrix inequality conditions is given to express the input and state constraints. Then, the Lyapunov function and average dwell time (ADT) approach are used to obtain sufficient conditions for the asymptotic stability of switched systems. Finally, two simulation examples are given to verify the effectiveness of the proposed method.

Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

AbstractWith the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane‐changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane‐changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach

AbstractA predictive queue management method is proposed for constrained congestion control in internet routers in the face of communication delays. The proposed method uses the queue and router models, input traffic rate, and queue length to precisely characterise the entire process. The model that has been built is then used to construct an optimal constrained active queue management (CAQM) strategy using the model predictive control method. Important factors, such as link capacity, Transmission Control Protocol (TCP) sessions, round‐trip time, and a few others, have been selected and used to linearise the interconnection of TCP. Then, an efficient MPC‐based structure to manage the CAQM in the face of unknown disturbances is designed. Simulations are used to validate the proposed method's effectiveness and robustness.