Model Predictive Control Theory Research Articles

Multi-Timescale Active and Reactive Power-Coordinated Control of Large-Scale Wind Integrated Power System for Severe Wind Speed Fluctuation

Wind speed fluctuations are extremely easy to cause the voltage change frequently. The existing voltage control methods are lacking in adjustment range and response speed. When wind speed fluctuates substantially or rapidly, voltage exceeding limit and the resulting grid safety problems cannot be avoided. Therefore, reasonably distributing and utilizing the reactive power of variable speed wind turbines in the whole network are an inevitable choice. However, the controllable reactive capacity of wind turbines is limited by converter capacity and active power, and thus, the grid demand may not be met. A new idea of active and reactive power-coordinated control for preventing voltage exceeding limit due to wind speed fluctuations was proposed. The effects of wind speed fluctuations on voltage were analyzed, and the controllable reactive capacity of variable speed wind turbines under wind speed fluctuations was studied. The relationship between voltage and the active and reactive power of wind farms was deduced. The theory and method of active and reactive power coordinated control before wind speed fluctuations were proposed based on the model predictive control theory. The control model based on multi-timescale was established. Finally, it was verified that the method can solve the voltage problems and maximize the system economy.

A Survey of Parallel Implementations for Model Predictive Control

Model Predictive Control (MPC) has its reputation since it can handle multiple inputs and outputs with consideration to constraints. However, this comes at the cost of high computational complexity, which limits MPC to slow dynamic systems. This paper provides an overview of the available methods to accelerate the MPC process. Various parallel computing approaches using different technologies were proposed to speed up the execution of MPC, some of these approaches are focused on building dedicated hardware for MPC using field programmable arrays (FPGA), and others are focused on parallelizing MPC computation using multi-core processors (CPUs) and many-core processors (GPUs). The focus of this survey is to review the available methods for accelerating MPC process. A brief introduction to the theory of MPC is provided first followed by a description of each approach. A comparison between the different methods is presented in terms of complexity and performance followed by a valid application for each approach. Finally, this paper discusses the challenges and requirements of MPC for future applications.

Optimization-Driven Control and Organization of a Robot Swarm for Cooperative Transportation

The cooperation of autonomous robotic systems, forming a robotic network by communicating with each other, brings about the potential to improve both performance and robustness in robotics applications, but introduces additional complexity in control design. In this context, this paper examines a cooperative transportation task that can serve as a test bed for distributed control algorithms, but may also inspire applications in logistics. In the proposed approach, the task solution is split into an organization scheme, determining formations useful for the current situation, as well as a distributed controller maintaining the formation. Both of these rely on the solution of optimization problems, with the latter drawing from distributed model predictive control theory. The optimization problems are formulated in a way that they can be derived automatically from the intrinsic properties of the considered scenario, e.g. the number of participating robots and the shape of the transported object. Therefore, no human intervention or scenario-specific design steps are necessary while still being able to cater to a wide range of scenarios. Challenging simulation scenarios illustrate the performance achievable with the proposed scheme.

A method to improve an electric vehicle’s range: Efficient Cruise Control

This article proposes a method to improve the range of an electric vehicle (EV) by controlling its speed depending on the upcoming traffic signal status. A conventional Adaptive Cruise Control (ACC) controls an EV’s speed such that there is a certain distance between the EV and the preceding vehicle. However, if the preceding vehicle drives at a non-optimum speed given the upcoming traffic signal status, as it nears the upcoming traffic signal the preceding vehicle will have to dissipate the unnecessarily gained kinetic energy by braking. If the EV blindly follows such a preceding vehicle using a conventional ACC, the EV will also have to dissipate a part of its kinetic energy by braking. In this work, this problem is addressed by proposing an Efficient Cruise Control (ECC). In the proposed ECC, if the EV is within a certain distance of the upcoming traffic signal, its speed reference is calculated based on the traffic signal status, while maintaining a minimum safe inter-vehicular distance from the preceding vehicle. The ECC is designed using Model Predictive Control theory and it is studied in a simulation environment using a Tesla S vehicle model. The simulation results show that the proposed controller improves the EV’s energy efficiency and therefore range, where the latter is an important bottleneck in widespread adoption of EVs.

Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system

This paper presents a novel braking torque distribution strategy for electric vehicles with four in-wheel motors equipped with the regenerative braking system. Safety and regeneration efficiency are the two major themes in the design of a regenerative braking system. Based on model predictive control (MPC) theory, the issues of multiple objectives and constraints of the regenerative braking system is well addressed. This proposed torque distribution controller can maximize the regeneration efficiency by determining the hydraulic braking torque and motor braking torque, which subject to the actuator constraints. Total braking torque of the four wheels tracks the braking requirement. Furthermore, in the case of ensuring brake safety, the braking torque of front and rear wheels are optimized to improve the possibility of energy recovery. The simulations have been conducted in Simulink environment based on an electric vehicle model established in AMESim software to verify the advantage of the proposed model predictive controller. In addition, the real-time test also demonstrates the effectiveness of the presented optimal control strategy.

Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control

Due to the randomness of the intermittent distributed energy output and load demand of a micro-energy-grid, micro-sources cannot fully follow the day-ahead micro-energy-grid optimal dispatching plan. Therefore, a micro-energy-grid is difficult to operate steadily and is challenging to include in the response dispatch of a distribution network. In view of the above problems, this paper proposes an integrated optimal dispatch method for a micro-energy-grid based on model predictive control. In the day-ahead optimal dispatch, an optimal dispatch model of a micro-energy-grid is built taking the daily minimum operating cost as the objective function, and the optimal output curve of each micro-source of the next day per hour is obtained. In the real-time dispatch, rolling optimization of the day-ahead optimal dispatching plan is implemented based on model predictive control theory. The real-time state of the system is sampled, and feedback correction of the system is implemented. The influence of uncertain factors in the system is eliminated to ensure steady operation of the system. Finally, the validity and feasibility of the integrated optimal dispatching method are verified by a case simulation analysis.

Active control method of large‐scale wind integrated power system with enhanced reactive power support for wind speed fluctuation

A regional power grid containing high-density wind power is generally connected to a load centre through long-distance transmission lines. Voltage problems, caused by wind speed fluctuation, in the sending-end grid have become increasingly serious. The variable speed wind turbine has been used to compensate the deficit in reactive power. However, the available methods could not meet the requirement because the controllable range of reactive power contributed by wind farms cannot be adjusted. A new idea of reactive power control realised by the active control before wind speed fluctuation was proposed to prevent the voltage variation. Firstly, the influence of wind speed fluctuation on voltage was analysed, and the power controllable range of wind turbines was studied. The principle and strategy of the active control of reactive power were proposed based on the model predictive control theory. Then, the active control model was established according to the system dynamic demand for reactive power. According to wind speed forecasting, the reactive control capability of wind turbine was excavated to meet the grid demand by adjusting active power before wind speed variation, and the reduced active power of wind farms is optimally compensated by thermal powers. Finally, the method was proven to solve voltage problems.

Adaptive condition predictive-fuzzy PID optimal control of start-up process for pumped storage unit at low head area

In order to overcome the rotational speed oscillation under no-load start-up condition caused by the ‘S’ characteristic area of the pump-turbine and the difficulty in improving the dynamic process under low head area of pumped storage unit (PSU), this paper uses the model predictive control and fuzzy logic control theory to study the optimal control of PSU under no-load start-up condition at low head area. Firstly, the instantaneous linearized controlled autoregressive integrated moving average model (CARIMA) of PSU is proposed. This model contains the dynamic equations of the complex pipeline system and pump-turbine generator-motor that accurately describe the hydraulic and mechanical dynamic characteristics. Based on the CARIMA, a novel adaptive condition predictive-fuzzy PID (ACP-FPID) controller is designed. The controller contains the operating point prediction and the avoidance of critical curves of the ‘S’ characteristic area. Then, multi-objective optimization control method combined with bacterial foraging algorithm is utilized for the optimal parameters selection of controller, guide opening time and frequency switching point when the closed-loop controller is put into operation. Furthermore, numerical simulation tests under start-up condition in typically low head are conducted to demonstrate the feasibility and robustness of the ACP-FPID optimal control method. The results indicate that the proposed method can effectively avoid the deep running of the unit into the ‘S’ characteristic area compared with traditional ones. The approximate periodic oscillations times and overshoot index of rotational speed obtained by ACP-FPID are less than 1 and 2% respectively, which are much smaller than that values obtained by the traditional ones. Moreover, strong robustness of ACP-FPID is testified when PSU disturbed by mechanical factors.

Potential field–based hierarchical adaptive cruise control for semi-autonomous electric vehicle

Adaptive cruise control, as a driver assistant system for vehicles, can adjust the vehicle speed to keep the appropriate distance from other vehicles, which highly increases the driving safety and driver’s comfort. This paper presents hierarchical adaptive cruise control system that could balance the driver’s expectation, collision risk, and ride comfort. In the adaptive cruise control structure, there are two controllers to achieve the function. The one is the upper controller which is established based on the model predictive control theory and used to calculate the desirable longitudinal acceleration. The collision risk is described by the Gaussian distribution. A quadratic cost function for model predictive control is formulated based on the potential field method through the contradictions between the tracking error, collision risk, and the longitudinal ride comfort. The other one is the lower optimal torque vectoring controller which is constructed based on the vehicle longitudinal dynamics. And it can generate the desired acceleration considering the anti-wheel slip limitations. Several simulations under different road conditions demonstrate that the proposed adaptive cruise control has significant performance on balancing the tracking ability, collision avoidance, ride comfort, and adhesion utilization. It also maintains vehicle stability for the complex road conditions.

The State-of-the-art of Model Predictive Control in Recent Years

Model predictive control is a control algorithm based on model and online application optimization performance. In the past 40 years, the feedback control strategy has been widely studied. However, with the rapid development of the economy, the requirements for online optimization and constrained performance have been improved, and the current model predictive control theory can not meet the demand any more. This paper first briefly describes the current situation of model prediction, industrial development, and application areas, and then analyzes the limitations of theory and technology at the current stage, then proposes the significance of the study of predictive control of large-scale systems, fast dynamic systems, and nonlinear systems for the development of model predictions.

Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young’s modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the “curse-of-dimensionality” associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.

Online Identification of Nonlinear Stochastic Spatiotemporal System With Multiplicative Noise by Robust Optimal Control-Based Kernel Learning Method.

In this paper, we propose a novel kernel method for the online identification of stochastic nonlinear spatiotemporal dynamical systems using the robust control approach. By the difference method, the stochastic spatiotemporal (SST) systems driven by multiplicative noise are first transformed into a class of multi-input-multi-output-partially linear kernel models (PLKMs) with heterogeneous random terms. With the help of techniques established for reproducing kernel Hilbert space, the online learning problem is reasonably considered as an output feedback control problem for a group of time varying linear dynamical systems. We develop an effective algorithm to address the learning problem of PLKM and SST systems by employing the model predictive control theory. Compared with the existing learning methods, the new one can achieve adaptive, robust, and fast convergent online modeling performance for the spatiotemporal dynamics with multiplicative noise, which greatly facilitates the characterization of physical characteristics of the system. Moreover, this investigation for the first time addresses the learning problems for SST systems with novel robust control techniques, which can provide some novel insights into the design of kernel machine learning methods from the perspective of optimal control theory. Numerical studies for benchmark systems are presented to illustrate the effectiveness and efficiency of our new method.

Overcoming Control Challenges in Urban Traffic

Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, Intending to reduce traffic congestion in urban areas due to the economical and environmental problems, analysis and control of existing infrastructures is indispensable, which in order improve safety and reduce the time spend by cars on the roads. This study compares a structured network-wide traffic controller based on Model Predictive Control (MPC) theory, which it represent a Simplified model (S model) and fixed-time (FT) controller. The fixed-time control strategy is executed in the traffic network, where the fixed-time signals are designed based on the data for the saturated scenario. Based on historical data, the fixed time assumes that the traffic patterns could be predicted accurately. On the other hand, MPC controllers are able to be efficient by reducing computation time and to be accurate too, in a systematic methodology to control multi-modal traffic lights based on a model predictive control approach. Then this study focus on two issues, the first one is the nonlinearity of the traffic prediction model and it is demonstrated how this model could be simplified to reduce the on-line computation time, then the complex urban road networks can be handled by the control system with more efficiently. The second issue is to propose a reduction of the congestion, within the model predictive control system based on internal flow model to minimize a cost function along a given time horizon.

Study on Integrated Control of AFS and DYC for Active Obstacle Avoidance

In order to realize the obstacle avoidance and improve the vehicle’s yaw stability, a chassis integrated controller which cooperated Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) is designed based on model predictive control (MPC) theory. It takes the corrective steering angle and desired yaw moment as inputs, and composes the driver-vehicle-road closed-loop system with the driver model. Performance of the controller is demonstrated as the continuous obstacle avoidance case. The simulation results show that the integrated controller can make the vehicle avoid the obstacle on both sides of the road and improve the yaw stability of the vehicle.

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

In recent years, unmanned surface vehicles (USVs) have received notable attention because of their many advantages in civilian and military applications. To improve the autonomy of USVs, this paper describes a complete automatic navigation system (ANS) with a path planning subsystem (PPS) and collision avoidance subsystem (CAS). The PPS based on the dynamic domain tunable fast marching square (DTFMS) method is able to build an environment model from a real electronic chart, where both static and dynamic obstacles are well represented. By adjusting the S a t u r a t i o n , the generated path can be changed according to the requirements for security and path length. Then it is used as a guidance trajectory for the CAS through a dynamic target point. In the CAS, according to finite control set model predictive control (FCS-MPC) theory, a collision avoidance control algorithm is developed to track trajectory and avoid collision based on a three-degree of freedom (DOF) planar motion model of USV. Its target point and security evaluation come from the planned path and environmental model of the PPS. Moreover, the prediction trajectory of the CAS can guide changes in the dynamic domain model of the vessel itself. Finally, the system has been tested and validated using the situations of three types of encounters in a realistic sea environment.

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

SUMMARYModel predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

Four-wheel independent drive electric vehicle was used as the research object to discuss the lateral stability control algorithm, thus improving vehicle stability under limit conditions. After establishing hierarchical integrated control structure, we designed the yaw moment decision controller based on model predictive control (MPC) theory. Meanwhile, the wheel torque was assigned by minimizing the sum of consumption rates of adhesion coefficients of four tires according to the tire friction ellipse theory. The integrated simulation platform of Carsim and Simulink was established for simulation verification of yaw/rollover stability control algorithm. Then, we finished road experiment verification of real vehicle by integrated control algorithm. The result showed that this control method can achieve the expectation of effective vehicle tracking, significantly improving the lateral stability of vehicle.

Occupant–vehicle dynamics and the role of the internal model

ABSTRACTWith the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occupant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant–vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver's or passenger's understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control (MPC) theory provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant–vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016.

Modeling of hydraulic fracturing and designing of online pumping schedules to achieve uniform proppant concentration in conventional oil reservoirs

We present a novel control framework for the closed-loop operation of a hydraulic fracturing process. Initially, we focus on the development of a first-principle model of a hydraulic fracturing process. Second, a novel numerical scheme is developed to efficiently solve the coupled partial differential equations defined over a time-dependent spatial domain. Third, a reduced-order model is constructed, which is used to design a Kalman filter to accurately estimate unmeasurable states. Lastly, model predictive control theory is applied for the design of a feedback control system to achieve uniform proppant concentration across the fracture at the end of pumping by explicitly taking into account the desired fracture geometry, total amount of proppant injected, actuator limitations, and safety considerations. We demonstrate that the proposed control scheme is able to generate a spatial concentration profile which is uniform and close to the target concentration compared to that of the benchmark, Nolte's pumping schedule.

Simulation analysis of adaptive cruise prediction control

Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.