MPC Controller Research Articles

Research on path tracking control of unmanned vehicle

With the continuous progress of science and technology, driverless vehicles have become one of the development trends of automobiles in the future. Path tracking control is a key problem in the research of driverless vehicles. Based on one control theory or by combining several different theories, different control algorithms are proposed, and some achievements have been made. This paper mainly studies the tracking control research method, which is a key problem in driverless vehicles. By analyzing and comparing the LQR control method and MPC control method, we compare their advantages and disadvantages, understand the concepts and related algorithms of various path-tracking control methods, and analyze the advantages and disadvantages of various control methods.

Spatiotemporal learning-based stochastic MPC with applications in aero-engine control

To deal with the issue of model uncertainty, extensive stochastic MPC methods have been investigated, where the uncertainties are typically assumed to follow given statistical distributions. However, in practical scenarios, the statistical properties of model uncertainties may depend on certain hyperparameters varying both temporally and spatially. For instance, in the applications of aero-engine control, different types of uncertainties may occur under different operational conditions, such as flight altitudes and Mach numbers, which are dynamically changing. Traditionally, the stochastic MPC methods may not be able to handle these types of uncertainties directly. Therefore, we propose a spatiotemporal learning-based stochastic model predictive control algorithm to study the stochastic optimal control problem with dynamically changing uncertainties, by constructing spatiotemporal Gaussian processes (GPs) to approximate the uncertainties based on measurement data. Since the spatiotemporal GPs may be difficult to evaluate with long processing time series, we present a state–space representation of GPs to employ computationally efficient Kalman filtering. Then, we derive a computationally tractable control strategy by parameterizing the controller and reformulating the cost and chance constraints, and analyze the corresponding recursive feasibility and closed-loop stability. Finally, the proposed algorithm is applied to the compressor control of the aero-engine, and the comparisons with other MPC controllers are demonstrated to show the effectiveness of our methods.

Three-Dimensional Guidance Approach with Obstacle Avoidance against Maneuvering Targets

A guidance approach with obstacle avoidance is proposed for maneuvering target interception. Firstly, by decomposing the constrained optimal guidance problem into two associated subproblems, the overall scheme of guidance is presented. Secondly, to improve the guidance accuracy against the maneuvering target, an MPC controller with disturbance estimation is designed, which transforms the global optimization problem into a finite-horizon optimal control problem. An auxiliary controller is also developed to ensure the system’s stability. Then, to achieve obstacle avoidance without affecting the guidance accuracy, a unified function is built to assess the threats from obstacles of different shapes, based on which the optimal guidance-obstacle avoidance model is established. Finally, to solve the above optimization problems, a hybrid solver is developed based on the adaptive moment estimation (Adam) algorithm, whose output will serve as the real-time guidance command. The numerical simulation results show that the guidance approach presented in this paper can achieve precise guidance and obstacle avoidance in various scenarios, and the Monte Carlo experiment further demonstrates the robustness and computation efficiency of the approach.

Indirect Shared Control Strategy for Human-Machine Cooperative Driving on Hazardous Curvy Roads

Effective and friendly human-machine cooperative driving is considered as a feasible solution to filling the gap between assisted driving and highly automated driving. This paper proposes an indirect shared control strategy, in both longitudinal and lateral directions, for safe cooperative driving on curvy roads, which often pose hazards in real-world driving. At the tactical decision level of the proposed strategy, driving authorities are dynamically allocated between human and machine based on the risk assessed with a data-driven gaussian processes regression (GPR) model, which is trained to generate dynamic safety envelopes of longitudinal and lateral control commands, namely vehicle speed and front wheel steering angle, from observed road conditions and vehicle states. At the maneuver execution level, a multi-objective hierarchical MPC controller is built to combine human-machine control commands smoothly for collision-free driving while simultaneously maintaining vehicle stability and mitigating human-machine conflicts. Both matching the driver's commands and tracking the reference trajectories are formulated into objectives of the optimization-based controller. The weight factor of each objective is adjusted according to the authorities allocated at the decision level. The results of experiments conducted on a 3 degree of freedom motion-base driving simulator show that the proposed strategy accomplishes effective and friendly cooperative driving on curvy roads. It's also verified that the proposed strategy is adaptable to unseen scenarios, mainly owing to the superiority of the GPR-based risk assessment model, driven by class-balanced training data, in modeling the nonlinearity and uncertainty of driving risks.

Dual Model Predictive Controlled Hybrid Energy Storage System for Naval DC Microgrids

Hybrid energy storage system (HESS) is an effective measure to improve the electrical performance of naval dc microgrids supplying pulsed power loads (PPLs). Coordination control scheme and capacity configuration of the HESS are two key issues to meet multiple control objectives and constraints. In response to the requirements of optimal operation for HESS under various complex scenarios, a dual model predictive control (D-MPC) strategy is proposed for the HESS integrated with the superconducting magnetic energy storage (SMES) and battery in this article. Firstly, the current reference of battery is obtained through the MPC integrated adaptive low-pass filtering (ALPF). Then, the local MPC controller of HESS converter is utilized to track the current reference of battery and SMES as well as the bus voltage reference to achieve transient power allocation of HESS and energy balancing of dc microgrid. Meanwhile, a cost function with adaptive weighting factor is designed to make tradeoff of conflict control objectives. Furthermore, the HESS capacity configuration and superconducting magnet optimal design according to its actual operation condition are presented. Comparative case studies are conducted in hardware-in-the-loop (HIL) experiment to demonstrate the superiority of the proposed scheme in terms of improving system operation performance under various PPLs.

Research on automatic emergency steering collision avoidance and stability control of intelligent driving vehicle.

In view of the need for emergency steering to avoid collision when the vehicle is in a dangerous scene, and the stability control of the vehicle during collision avoidance. This paper proposes a planning and control framework. A path planner considering the kinematics and dynamics of the vehicle system is used to formulate the safe driving path under emergency conditions. LQR lateral control algorithm is designed to calculate the output steering wheel angle. On this basis, adaptive MPC control algorithm and four-wheel braking force distribution control algorithm are designed to achieve coordinated control of vehicle driving stability and collision avoidance safety. The simulation results show that the proposed algorithm can complete the steering collision avoidance task quickly and stably.

Control Strategy of Ball Mill Based on Disturbance Observer and a Virtual Analyzer of Overload

Purpose of research.. Increasing the ore productivity of the grinding mill under the influence of external disturbances, preventing overloading of the mill in operating conditions close to overloading.Methods. To achieve this goal, it is proposed a new automatic control system (ACS) for ore volumetric filling of grate-discharge ball mill in a closed grinding cycle using model predictive control and active disturbance observer (MPC-DOB). And in addition, virtual analyzer (VA) of the ore weight in the mill based on the developed model of the grinding process is proposed for mill overload control. The ACS was tested on a laboratory installation with the mill PC-model in Simulink and the PLC based implementation of control algorithms. Results. MPC-DOB was compared with other ACS based on PID, MPC controllers for various test scenarios and show high performance under the influence of sinusoidal and step disturbances by reducing relative standard deviation (RSD) by 4-7 %. The combined using of MPC-DOB and VA made it possible to increase the grinding process ore productivity by 1 % and improve the quality of mill vibration stabilization in the mode of functional instability. Conclusion. The developed ACS can be used in the process control system for grinding in a ball mill with a grate to increase the productivity and stability of the technological process and reduce the energy consumption of the mill drive.

Temperature sensitivity characteristics of PEM fuel cell and output performance improvement based on optimal active temperature control

This paper investigates the influence of temperature on the polarization process of PEM fuel cell in detail, and the quantitative analysis of temperature sensitivity for the polarization process is performed based on the electrochemical impedance spectroscopy and the semi-empirical equivalent circuit model. Besides, in order to make the PEM fuel cell stack operates with the optimal working efficiency and the lowest degradation and fault rate, an active temperature tracking decoupling control approach based on coupling algorithm of feedforward control, Takagi-Sugeno fuzzy theory and model predictive control is proposed. To fully demonstrate the effectiveness and superiority of the proposed method, its control effect on the 85 kW commercial PEM fuel cell system is compared with traditional feedforward control, PID control and MPC control under different operation scenarios. The results prove that the proposed method have better robustness and dynamic performance under different working conditions. Besides, the proposed decoupling tracking control model can keep the temperature difference between the inlet and outlet of the stack within 6 °C under the dynamic current condition, which is more helpful to enhance the uniformity of the internal temperature of the PEM fuel cell stack.

Avoiding module capacitance over-dimensioning of Modular Multilevel Converters with constrained and unconstrained modulator-based Model Predictive Control

The power density of Modular Multilevel Converters (MMCs) is often limited by the over-dimensioning of the module capacitance value. This is common because with conventional control schemes, large voltage and energy margins must be included in the MMC design to avoid overvoltages in the module capacitors and saturation effects due to undervoltages in the module capacitors. This paper presents two Model Predictive Control methods that can reduce the necessary voltage and energy margins to a minimum and therefore increase the power density of the MMC system compared to conventionally controlled MMCs. A trade-off between computational effort and module capacitance over-dimensioning is discovered for the proposed constrained MPC. The unconstrained MPC is compared to the constrained MPC and conventional PI control using simulations. Finally, the performance of the unconstrained MPC is experimentally verified with an MMC system prototype and compared to a traditional PI control system.

Evaluation of Istanbul Strait (Bosphorus) passage simulations in terms of CO2 emissions

It is crucial to reduce CO2 emissions due to marine transportation, which has a direct effect on air pollution in channels such as the Istanbul Strait (Bosphorus), which has a high human population density in the vicinity. In this study, it is tried to evaluate whether it is possible to reduce CO2 emissions during the passage of the Bosphorus. For this purpose, a mathematical model including three different control systems, called MPC, PD, and PD-Fuzzy controllers, is created to evaluate the controller effect on CO2 emissions during the passage. Each control system is selected separately from the mathematical model and the Bosphorus passages are simulated. As a result of these simulations, the velocity of the ship, the route to be followed by the ship, and the change of the ship's rudder angle throughout the passage are obtained using each control system. In these simulations, it is assumed that there are no external forces, such as wind and current. The obtained results from the simulations are accepted as a reference and an evaluation is made in terms of CO2 emissions by including the number of commercial ships passing through the Bosphorus between 2006 and 2019. The calculated annual CO2 emissions that may occur in the Bosphorus passage with the various control systems are compared. The CO2 emissions increases approximately 1.86% depending on the selection of the control system for the determined ship for the Bosphorus passage simulations. As a result, it is emphasized that a more effective control system for reducing CO2 emissions in narrow channel passages such as the Bosphorus can exist.

A new composite controller for trajectory tracking control of a piezo-driven bridge displacement amplifier

In this paper, a new composite controller (feedforward + feedback) is proposed for the trajectory tracking control of a piezo-driven bridge displacement amplifier. A hysteresis model considering dynamic effects is established based on the modified Bouc–Wen (MBW) model, which can describe the asymmetry and frequency-dependence behaviors. The effectiveness of the modified model is verified by the experimental comparison. A feedforward controller is designed to compensate for hysteresis nonlinearity based on the inverse multiplicative structure of the modified model. A feedback controller with the Kalman observer is designed based on a modified model predictive control (MMPC). The proportional term and integral term of feedback error are introduced into the MPC control law to improve the response speed and reduce the steady-state error. The proposed MBW + MMPC is compared with MBW + proportional–integral–derivative (PID), and MBW + MPC. The experimental tracking results of continuously differentiable signals and non-differentiable signals verify MBW + MMPC feasibility and show that it can improve tracking performance.

Energy Demand Management in an Industrial Manufacturing Plant using MPC and Neurofuzzy Models

An MPC controller is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system which has several machines, renewable generation resources, a combined heat and power (CHP) generator for power production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the price of the electricity market, to reduce the cost. The use of neurofuzzy models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical forecasting variables (wind speed, global irradiance, etc.).

Investigations on Speed Planning Algorithm and Trajectory Tracking Control of Intersection Scenarios Without Traffic Signs

Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. The degree of response to other traffic participants and whether the path tracking effect is good are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to deal with these problems, in this paper, the frame structure of POMDP (Partially Observable Markov Decision Processes) and the time distance coordinate system are combined to carry out the speed planning for the self-propelled vehicle. The speed planning designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs. Specifically, the method is considered both the driving position and the time range of arrival of the vehicle at the intersection in real-world situations. At the same time, the algorithm is projected the state information into the time distance coordinate system in the form of probability, and used the value iteration method to solve the optimal solution, so as to improve the adaptability of the algorithm to real-world scenarios. Furthermore, MPC control is added on this basis. Rational use of dynamic MPC and kinematic MPC can effectively reduce the tracking error, improve the stability of tracking, and carry out finer control when the speed frequently fluctuates.

Energy-Based Cooperative Distributed MPC for Frequency Control in Microgrids

Energy-Based Cooperative Distributed MPC for Frequency Control in Microgrids

Distributed MPC for Trajectory Tracking and Formation Control of Multi-UAVs With Leader-Follower Structure

Distributed MPC for Trajectory Tracking and Formation Control of Multi-UAVs With Leader-Follower Structure

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

Evaluation of optimisation techniques on the performance of an on-line MPC controller in an occupancy grid for autonomous mobile robots

Computational capabilities on embedded platforms, especially for autonomous mobile robotics, enable them to be used for more challenging tasks, e.g., to apply advanced control methods like an on-line MPC controller on the robot itself. In the last decades, many improvements in direct methods to solve the optimal control problem were proposed, which speed up the optimisation process. We apply the multiple shooting approach to solve the Optimal Control Problem in a parallel manner. We apply the on-line MPC scheme with the multiple shooting approach in simulations, assess them in a hardware-in-the-loop scenario and conduct experiments on a Raspberry Pi 4-based differential-drive-platform to assess the realtime capability to control the robot while the optimisation is carried out locally.

A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming

Single-lane car-following is a fundamental task in autonomous driving. A desirable car-following controller should keep a reasonable range of distances to the preceding vehicle and do so as smoothly as possible. To achieve this, numerous control methods have been proposed: some only rely on local sensing; others also make use of non-local downstream observations. While local methods are capable of attenuating high-frequency velocity oscillation and are economical to compute, non-local methods can dampen a wider spectrum of oscillatory traffic but incur a larger cost in computing. In this letter, we design a novel non-local tri-layer MPC controller that is capable of smoothing a wide range of oscillatory traffic and is amenable to real-time applications. At the core of the controller design are 1) an accessible prediction method based on ETA estimation and 2) a robust, light-weight optimization procedure, designed specifically for handling various headway constraints. Numerical simulations suggest that the proposed controller can simultaneously maintain a variable headway while driving with modest acceleration and is robust to imperfect traffic predictions.

Smartenance DC-DC On-Board Power Converters

Traditional marine power grids, using diesel motors/generators, pollute the atmosphere with their CO2 emissions and adverse particles. Nowadays, green energy supplies like solar systems and fuel cells play significant roles in the electrification of marine propulsion plants. In general, marine power systems with green energy units are often considered as a kind of mobile DC microgrids under constant power loads (CPLs). However, since the CPLs impose nonlinearity and negative impedance instability on marine power systems, traditional linear controllers no longer stabilize the system under various disturbances. This motivated this brief to develop a non-integer model predictive control for the accurate control of DC/DC converters with CPLs to stabilize the main bus voltage and current in the full-electric ferry ship. In practice, the stability of ship systems is highly threatened due to the negative-independence effect of CPLs. To address the adverse challenges of CPLs, the deep deterministic policy gradient (DDPG) with actor and critic neural networks (NNs) is applied to effectively design the coefficients embedded in the non-integer MPC. In this approach, the actor NN provides the regulatory signals to adjust the non-integer MPC controller while the critic NN is trained to evaluate the quality of actions of actor NN. The experimental results with comparative analysis are carried out to validate the accuracy of the non-integer MPC under the change in the CPL’s power. The results demonstrated that the proposed controller offers a higher level of robustness than the state-of-art-schemes.

Stochastic MPC for energy hubs using data driven demand forecasting

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.