Combination Of Model Predictive Control Research Articles

Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning

The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.

Advanced overpressure protection: A concept for smart controlled safety pressure relief systems

Safety devices such as safety valves and rupture discs are crucial components in protecting chemical reactors from exothermic runaway reactions and overpressure scenarios. While traditional safety valves operate autonomously, controlled safety devices offer the potential for dynamic adjustments to optimize system performance. However, existing safety valves and controlled safety pressure relief systems (CSPRS) lack flexibility in a fixed relief pressure and stroke positions. This article introduces a new smart overpressure protection device (SmOP) with an adaptable safety device controlled through a combination of model predictive control and PID-control. Tested extensively in a high-pressure loop, the SmOP demonstrates rapid and precise control behavior, ensuring reactor safety as well increasing the efficiency. The control concept, based on model prediction followed by PID-correction, is validated through endurance tests and various opening conditions. Results indicate the SmOP's robustness against external disturbances and its potential for industrial applications. Future work includes developing a failsafe concept and laboratory tests with chemical reactions to further validate the SmOP's performance.

Energy-efficient semi-continuous distillation of a ternary mixture using combined tracking-economic model predictive control

Distillation is an energy-intensive separation technique. Semicontinuous distillation has gained attention as a cost-effective alternative to conventional multi-column distillation of multi-component mixtures. However, the cyclic behavior of semicontinuous distillation poses operational challenges considering cost of energy and need for maintaining consistent product purity. The challenge is addressed in this work by proposing a dual-objective Model Predictive Controller (MPC) that handles both product purity and energy consumption through a combined tracking-economic objective function. The proposed MPC features Extended Kalman Filter for state estimation and the successive linearization technique for building the prediction model. The nonlinear plant model is implemented in OpenModelica, which is linked to Matlab for MPC implementation. The effectiveness of the proposed MPC is demonstrated on semicontinuous distillation of Benzene, Toluene, and o-Xylene. The dual-objective MPC is shown to yield an energy saving of 8% per feed processed compared with conventional tracking MPC, while also performing well under process disturbances. It is also shown that the feed processed during a certain time period is 8% higher in the dual-objective MPC than the tracking MPC. The considerable economic improvement is gained without degrading the product purities, indicating that dual-objective MPC is an effective approach to energy-efficient operation of semicontinuous distillation.

Moving horizon estimation and control of a binary simulated moving bed chromatographic processes with Langmuir isotherms

Simulated moving bed chromatographic (SMB) chromatographic processes are widely used for separations in pharmaceutical and biotechnological industries. In the present work, the control of such processes is proposed based on a semi-centralized control scheme that utilizes a combination of model predictive control (MPC) and classical PID control. The necessary information from the process, i.e. the internal concentration profiles, for the MPC is obtained by a moving horizon estimator (MHE) in real-time from the available limited measurements (the cycle-averaged concentrations of the extract and raffinate product streams and the dimensionless retention times of the concentration waves in the regeneration zones). As a test case study, the separation of a hypothetical system governed by the Langmuir isotherms in the nonlinear concentration range of the isotherms is considered. First, the stand-alone MHE is validated in open loop mode with no plant-model mismatch under deterministic and stochastic conditions. In the latter case, the true measurements are subject to random normally distributed noise. To evaluate the performance of the proposed control strategy, a reference tracking (change of the requirements for both the purities and the retention times) scenario is simulated. The investigated scenarios are: (i) no plant-model mismatch, (ii) MHE with ideal noiseless measurements and finally (iii) MHE under the influence of measurement noise. Results show that the controller is able to follow the change of the references from reduced purity to complete separation and vice versa closely.

Fault-tolerant control for satellite autonomous rendezvous with quality characteristics and actuator uncertainties

An autonomous fault detection and fault-tolerant control solution combining Model Predictive Control (MPC) and Label Distributed Learning (LDL) is developed for the position tracking and attitude synchronization problems in autonomous satellite rendezvous and docking. The proposed algorithm can deal with center of mass (CM) variations and time-varying mass characteristics due to satellite fuel consumption, actuator saturation and fault, and external disturbances. Firstly, a six-degree-of-freedom (6-DOF) satellite translation-rotation coupling model was established to express the relative motion between the satellite and the target, taking the above factors into account. Subsequently, an on-board autonomous closed-loop fault-tolerant control algorithm using MPC is proposed, in which an LDL-based fault detection mechanism is embedded. The scheme specifies that propulsion faults are estimated by calculating the difference between the MPC's prediction of the satellite's future state and the measured change in the actual state. And the mapping matrix between the commanded force/torque and the actual force of the propulsion system is updated in real time to achieve autonomous fault detection and compensation on board the satellite. Among them, the LDL-based fault detection scheme is trained offline. It requires only simple matrix operations for on-orbit use, which is low-cost and suitable for resource-limited space environments. The additional creation of a time-varying disturbance observer allows the estimation and compensation of unmodelled errors and external disturbances. The stability of the proposed fault-tolerant control algorithm is rigorously demonstrated using Lyapunov analysis. Finally, numerical simulations show that the proposed method can successfully achieve autonomous satellite docking under different combinations of thruster faults.

High‐level decision‐making for autonomous overtaking: An MPC‐based switching control approach

AbstractThe key motivation of this paper lies in the development of a high‐level decision‐making framework for autonomous overtaking maneuvers on two‐lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high‐level decision‐making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high‐level decisions. It establishes a crucial connection between high‐level decision‐making and low‐level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours.

Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Longitudinal and lateral stability control for autonomous vehicles in curved road scenarios with road undulation

PurposeThis research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.Design/methodology/approachIn addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.FindingsThe framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.Originality/valueThe proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes

This paper investigates the combination of model predictive control (MPC) concepts and posterior sampling techniques and proposes a simple constraint tightening technique to introduce cautiousness during explorative learning episodes. The provided theoretical analysis in terms of cumulative regret focuses on previously stated sufficient conditions of the resulting ‘Cautious Bayesian MPC’ algorithm and shows Lipschitz continuity of the future reward function in the case of linear MPC problems. In the case of nonlinear MPC problems, it is shown that commonly required assumptions for nonlinear MPC optimization techniques provide sufficient criteria for model-based RL using posterior sampling. Furthermore, it is shown that the proposed constraint tightening implies a bound on the expected number of unsafe learning episodes in the linear and nonlinear case using a soft-constrained MPC formulation. The efficiency of the method is illustrated using numerical examples.

Path planning algorithm in complex environment based on DDPG and MPC

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environments. However, the paths formed by grid edges can be longer than the actual shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. In this paper, Matplotlib is used for simulation experiments. The results show that our method has improved considerably in accuracy by 8.11% -43.20% compared with theother methods, and on the length of the path and turning angle by reducing about 50 units and 340 degrees compared with DeepQ Network (DQN), respectively. We also employ Unity 3D to perform simulation experiments in highly uncertain environments, such as squares.

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers.

Design and assessment of a core-power controller for lithium-cooled space nuclear reactor based on the concept of fuzzy model predictive control

Thanks to its unique characteristics of high power-to-mass ratio, shallow reactivity poisoning, and quick response to reactivity control, power supply system based on lithium-cooled space nuclear reactor is preferred for various exploration missions into outer and deep space. However, due to its nature of few-people or even unmanned on-duty, an intelligent autonomous control of the reactor system, especially an accurate control of the reactor core power following the demanding power output, is of vital importance for such a space nuclear reactor. In this study, a core-power controller for a megawatt ultra-small lithium-cooled space nuclear reactor was designed based on the concept of fuzzy model predictive control (FMPC) combining model predictive control and T-S fuzzy theory. Performance of the FMPC controller was simulated and assessed with the Simulink platform for five typical operation transients including ramp, step and disturbance transient. The results show that the intelligent FMPC controller possesses an excellent load-following ability and anti-interference ability, both of which are of vital importance for space exploration missions. When compared with the classical PID controller, the FMPC controller designed in this study shows also a much better performance with smaller overshoot, lesser adjusting time and lower integral time-squared error.

Hierarchical Vessel Autonomous Operation in a Port with Safety Certificates: Combined MPC and CBF Approach

In this paper, we investigate autonomous vessel operation in/near a port and present a novel hierarchical control architecture that combines model predictive control (MPC) and control barrier function (CBF). Our architecture is composed of two layers, navigation and control, and switches control schemes as the ideal vessel operations vary depending on the vessel locations. Specifically, we divide the operation into three phases, namely approaching phase, breakwater passing phase, and docking phase. In all phases, we employ the CBF-based online optimization with a vessel kinematic model at a lower layer to certify safety even in the presence of the prediction error in the navigation layer. The navigation layer is designed based on MPC so as to smoothen the collision avoidance behavior and to reflect various specifications stemming from laws. The present control architecture is demonstrated through various simulations including the one with real data of vessels in Tokyo Bay.

Active Fault-Tolerant Control Based on MPC and Reinforcement Learning for Quadcopter with Actuator Faults

In this paper, we propose an active fault-tolerant control (AFTC) method combining model predictive control (MPC) and reinforcement learning (RL) for the quadcopter with actuator faults. We take a data-based discriminant model as the fault detection and diagnosis (FDD) module indicating a system fault mode based on the state error. With the information of the fault mode and the state error, the RL controller generates auxiliary control signals to correct the system. To configure the MPC controller quickly, we propose an auxiliary signal-based method for estimation of fault parameters and prove its convergence. The AFTC framework reduces requirements for accurate modeling, and avoids the instability of the RL controller under a continuous operation. To validate the effectiveness of the proposed framework, two trajectory tracking simulations with single and multiple faults are carried out. The simulation results show satisfactory performance and verify that the proposed framework is real-time applicable.

Stability Study of Time Lag Disturbance in an Automatic Tractor Steering System Based on Sliding Mode Predictive Control

To improve the working accuracy and anti-interference capability of the steering operation of an automatic tractor, this paper investigates the tractor steering system. In response to the current problems of high steering resistance during tractor field operations and the low service life of the drive shaft of conventional electric steering wheel solutions, an electro-hydraulic coupled power-assisted solution combining EPS and HPS is proposed. The combination of the EPS system’s high control accuracy and sensitive steering operation and the hydraulic power system’s large steering torque greatly reduces the power of the power motor and battery performance requirements, optimizing the power transmission scheme to achieve green and energy-saving purposes. Secondly, the research is focused on the influence of external disturbances on the stability of the steering system during tractor operation, and a combination of model predictive control and sliding mode control is used to study the steering system control strategy. It is finally demonstrated through simulations and experiments that it can compensate for the disturbance of the system control parameters by external disturbances, has the ability of MPC to handle input constraints and maintains the advantages of SMC robustness.

Squat motion of a bipedal robot using real‐time kinematic prediction and whole‐body control

AbstractSquatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high‐performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole‐body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real‐time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)‐based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed‐form RKP is greatly reduced. The RKP method is verified in simulation on a heavy‐loaded bipedal robot. The robot makes rapid and large‐amplitude squatting motions, which require close‐to‐limit torque outputs. Compared with the conventional QP‐based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.

Intelligent air defense task assignment based on hierarchical reinforcement learning.

Modern air defense battlefield situations are complex and varied, requiring high-speed computing capabilities and real-time situational processing for task assignment. Current methods struggle to balance the quality and speed of assignment strategies. This paper proposes a hierarchical reinforcement learning architecture for ground-to-air confrontation (HRL-GC) and an algorithm combining model predictive control with proximal policy optimization (MPC-PPO), which effectively combines the advantages of centralized and distributed approaches. To improve training efficiency while ensuring the quality of the final decision. In a large-scale area air defense scenario, this paper validates the effectiveness and superiority of the HRL-GC architecture and MPC-PPO algorithm, proving that the method can meet the needs of large-scale air defense task assignment in terms of quality and speed.

Predictive energy management with engine switching control for hybrid electric vehicle via ADMM

This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53ms. Our results demonstrate that the proposed ADMM is capable of real-time control.

Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight.

Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification. Specifically, as the number of connections falls below a critical threshold, flight performance drops considerably. We develop sparsification paradigms and explore their limits for control tasks. Monte Carlo simulations also quantify the statistical distribution of network weights during pruning given initial random weights of the DNNs. We demonstrate that on average, the network can be pruned to retain a small amount of original network weights and still perform comparably to its fully-connected counterpart. The relative number of remaining weights, however, is highly dependent on the initial architecture and size of the network. Overall, this work shows that sparsely connected DNNs are capable of predicting the forces required to follow flight trajectories. Additionally, sparsification has sharp performance limits.

Path tracking control strategy for off-road 4WS4WD vehicle based on robust model predictive control

With the increasing requirements for vehicle adaptability and maneuverability in various road environments, four-wheel-steering four-wheel-drive (4WS4WD) vehicles have attracted wider attention. This paper presents a robust model predictive control-based strategy for the path tracking of 4WS4WD vehicles considering external disturbances. The strategy combines model predictive control (MPC) and control allocation under an upper–lower structure. The main objective of the present work is to improve the robustness and stability of path tracking by developing an MPC algorithm in the upper layer. The controller design considers general disturbances caused by allocation errors and sudden disturbances caused by an outer force in the offset model. Based on the offset model, a robust MPC control law is obtained by converting the robustness constraints into a linear matrix inequality. The control law is mathematically demonstrated to be stable in multidisturbed conditions via the Lyapunov stability theorem. Through comparison with a similar control algorithm of path tracking and applying it on different uneven ground conditions, the proposed robust algorithm is found to effectively overcome disturbances on the system.