Predictive Control Algorithm Research Articles

Smart Path Generation using Model Predictive Control

This paper presents an investigation into path generation techniques using Model Predictive Control (MPC) and Pure Pursuit algorithms, implemented and evaluated in a simulated environment using Python. The objective of the study was to compare the performance of these two approaches in terms of path tracking and obstacle avoidance. The research focused on the applicability of MPC and Pure Pursuit algorithms in autonomous navigation systems, with a specific emphasis on addressing the challenge of generating smooth and dynamically feasible paths while ensuring collision avoidance. The simulation environment provided a platform for conducting experiments, allowing for testing and analysis of the algorithms. The results of the study demonstrated that MPC successfully generated paths while effectively avoiding obstacles. The MPC algorithm exhibited robustness and adaptability to dynamically changing environments, allowing the autonomous agent to navigate through complex scenarios. However, the investigation also revealed a limitation with Pure Pursuit in terms of curvature volatility. The Pure Pursuit algorithm showed inconsistent performance due to abrupt changes in curvature, which impacted the smoothness and stability of path tracking. Overall, this research highlights the significance of selecting an appropriate path generation algorithm based on the specific requirements of the autonomous navigation system. The study serves as a foundation for future investigations and advancements in path planning and control techniques, enabling the development of more efficient and reliable autonomous systems.

A Novel Low-Complexity Cascaded Model Predictive Control Method for PMSM

A novel low-complexity cascaded model predictive control method for permanent magnet synchronous motors is proposed to achieve a fast dynamic response to ensure the system’s steady-state performance. Firstly, a predictive speed controller based on an extended state observer is designed in the outer speed loop to improve the anti-interference ability of the system; then, a low-complexity three-vector predictive control algorithm is adopted in the current inner loop, taking into account the steady-state performance of the system and lower computational burden. Finally, a comparative analysis is conducted between the proposed method and traditional methods through simulation and experiments, proving that the proposed method performs well in dynamic and static performance. On this basis, the computational complexity of the current inner loop three-vector prediction algorithm is effectively reduced, indicating the correctness and effectiveness of the proposed method.

Stabilizing model predictive control synthesis using integral quadratic constraints and full‐block multipliers

SummaryIn this paper, we discuss how to synthesize stabilizing model predictive control (MPC) algorithms based on convexly parameterized integral quadratic constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur'e systems subject to sector‐bounded and slope‐restricted nonlinearities. As the main novelty, we introduce point‐wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed‐loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature.

EMPMR berthing scheme: A novel event-triggered motion planning and motion replanning scheme for unmanned surface vessels

In this study, the issues related to approaching and berthing of unmanned surface vessels (USVs) are investigated. The automatic berthing task is divided into motion planning and motion replanning. First, a smooth path from the initial position to the berth is planned using A* algorithm and B-spline. Then, the segmented interpolation method is employed to let the USV velocities converge slowly to zero at the berth, and an initial berthing trajectory is obtained. Moreover, considering the kinetic constraints of the USV, the nonlinear model predictive control (NMPC) algorithm is employed in the offline motion planning and online motion replanning scheme. In this scheme, novel static, and COLREGs-based dynamic obstacle avoidance functions are proposed based on Gaussian functions. Furthermore, the event-triggered mechanism is adopted to improve the real-time performance of the NMPC algorithm. The proposed event-triggered motion planning and motion replanning (EMPMR) scheme and Han et al.’s scheme are tested in six scenarios. Simulation results show that the proposed EMPMR scheme can accomplish USV’s approaching and automatic berthing of USVs and has better obstacle avoidance and computational performance.

Robust model agnostic predictive control algorithm for randomly excited dynamical systems

We propose a novel robust model agnostic predictive control (RoMAn-MPC) algorithm and illustrate its application in randomly excited dynamical systems. Unlike conventional model predictive control algorithms, the proposed RoMAn-MPC requires no information about the underlying physics of the system; instead, the governing physics is identified by using the recently proposed stochastic equation discovery framework. One key advantage of the proposed approach resides in its capability to generalize; this eliminates the repeated retraining phase — a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed RoMAn-MPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.

Multi‐rate event‐triggered control with imperfect data for dense medium separation

AbstractThe stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method.

Double voltage vector model predictive control for grid-connected cascade H-bridge multilevel inverter with fixed switching frequency

Model predictive control (MPC) is an outstanding control method which can achieve superior dynamic response, nonlinear control and multi-objective collaborative control. However, because of unfixed switching frequency, the harmonic spectrum of the output current is dispersed, which would make it difficult to design the output filter. In this work, a double voltage vector model predictive control (DVV-MPC) algorithm for grid-connected cascade H-bridge (CHB) multilevel inverter is presented. The algorithm not only has the advantages of MPC but also produces fixed the switching frequency of the inverter. It is realized by a modulation stage with two adjacent voltage vectors in one switching cycle. And the duty cycle of the associated voltage level is obtained by minimizing an additional cost function. This method is suitable for the cascades of n cells inverter. For reducing the computation burden and eliminating the computation delay, adjacent regions prediction method and time-delay compensation method are applied to the inverter. Finally, in this paper, the proposed strategy is applied to a single-phase grid-connected 7-level CHB inverter. Simulation and experiment are executed to show the advantages of the proposed control algorithm in dynamic state and steady state.

Event-triggered distributed predictive cooperation control for multi-agent systems subject to bounded disturbances

This paper presents an event-triggered distributed predictive cooperation strategy for dynamically decoupled subsystems subject to bounded disturbances. Different from the traditional cooperative control using coupled constraints related to the agent’s state information, a synchronization parameter related to the agent’s control input information is introduced to design the parameterized synchronization constraint for the cooperation of multi-agent subsystems. An event-triggering condition involving the error between the system state and its optimal prediction is first designed for each agent, and the event-triggered distributed predictive control algorithm that is established on the triggering mechanism and the dual-mode approach is then designed. In such a framework, the distributed optimization problem is solved, and the parameterized information is exchanged between agents only when the triggering condition is satisfied, reducing the computation and communication load more efficiently. Moreover, the theoretical results guaranteeing the feasibility and closed-loop stability are developed. Finally, a formation control example for multiple mobile robots is given to verify the effectiveness of the proposed strategy.

Model predictive and SoC balancing control of a CHB inverter in battery energy storage application using reward functions

SummaryThis article presents an improved model predictive current control algorithm combined with a novel state of charge (SoC) balancing approach for a three‐phase cascaded H‐bridge inverter in battery energy storage applications. Based on the model predictive control with elimination of redundant voltage vectors, neighboring vectors of the last voltage vectors are used to find the optimal ones in only one control cycle, which solves the problems of high computational burden and slow dynamic response. To overcome the unsuitability of the conventional cost function in SoC balancing, the reward functions that effectively balance the inter‐phase SoCs, inner‐phase SoCs, and inner‐phase dc voltages are presented respectively. Simulation and hardware‐in‐the‐loop‐based experimental results validate the effectiveness of the proposed strategy.

Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles.

Introduction: The challenge of navigating a Mobile robot in dynamic environments has grasped significant attention in recent years. Despite the available techniques, there is still a need for efficient and reliable approaches that can address the challenges of real-time near optimal navigation and collision avoidance. Methods: This paper proposes a novel Log-concave Model Predictive Controller (MPC) algorithm that addresses these challenges by utilizing a unique formulation of cost functions and dynamic constraints, as well as a convergence criterion based on Lyapunov stability theory. The proposed approach is mapped onto a novel recurrent neural network (RNN) structure and compared with the CVXOPT optimization tool. The key contribution of this study is the combination of neural networks with model predictive controller to solve optimal control problems locally near the robot, which offers several advantages, including computational efficiency and the ability to handle nonlinear and complex systems. Results: The major findings of this study include the successful implementation and evaluation of the proposed algorithm, which outperforms other methods such as RRT, A-Star, and LQ-MPC in terms of reliability and speed. This approach has the potential to facilitate real-time navigation of mobile robots in dynamic environments and ensure a feasible solution for the proposed constrained-optimization problem.

Model predictive control to account for prolonged changes in insulin requirements following exercise in type 1 diabetes

Automated insulin delivery systems are becoming increasingly available in the treatment of type 1 diabetes. They can improve glycemic outcomes while reducing patient burden, but good glycemic control during and after exercise remains challenging. Exercise causes substantial and prolonged changes in insulin sensitivity that consequently affect insulin requirements, and can lead to late-onset hypoglycemia if not accounted for.Here, we present a model predictive control algorithm that adjusts insulin delivery during the recovery period to improve glycemic outcomes after aerobic exercise. The algorithm continuously estimates insulin sensitivity from glucose measurements via an unscented Kalman filter. It integrates the estimate by continuously updating the target insulin input as well as the process model to account for changing insulin demands. The proposed approach is generic and transferable to other control formulations.We evaluate our new control strategy in-silico using a validated diabetes patient model with aerobic exercise. We consider a virtual patient population in full-day simulations including a wide variety of exercise scenarios covering moderate to high intensities and different timing and duration of the exercise. We demonstrate improved glycemic outcomes over night following a day with exercise for all scenarios and show robustness of our approach to common disturbances.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data. Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403. Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred. AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia. JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Real-Time Control Operation Method of Water Diversion Project Based on River Diversion Disturbance

Changes in water diversion flow are the major disturbance sources in the daily operation of water diversion projects. Ensuring efficient and safe project operation while dealing with different degrees of water diversion disturbance is crucial for real-time control operation. Based on the historical water diversion projects in China and abroad, this study constructs the water diversion disturbance conditions, selects the typical disturbance lines, and constructs the control objectives for different water diversion disturbance lines. The discrete state space equation of the multi-channel pool integral time-delay model is introduced and used as the system prediction model. Concurrently, the simulation results of the river channel hydrodynamic model are used to correct the system state. The model predictive control algorithm is established according to the objective functions of different typical water distribution disturbance lines, and the control strategy of the control gate and pump station along the water diversion project is formulated to assist in the decision making of the project scheduling operation scheme. The proposed method can better cope with different degrees of river diversion disturbance, compensate for the loss of control performance caused by the low accuracy of the generalized model simulation, and improve water level control and sluice regulation.

Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers’ steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.

Vehicle Obstacle Avoidance Path Tracking Control Based on Constraint MPC

In the process of obstacle avoidance, there are some problems such as low precision and poor safety in the path tracking of intelligent vehicles. To solve these problems, a model prediction controller with soft constraints is designed in this paper. The controller adopts the vehicle motion model, according to the radius of the circumcircle of the obstacle and the vehicle, and the anti-collision constraint condition is constructed to improve vehicle safety. The vehicle tracks its path using a model predictive control (MPC) algorithm. Sideslip angle and acceleration control variables were introduced as soft constraints to improve vehicle path tracking accuracy. The simulation was carried out by CarSim and MATLAB/Simulink platforms. The results show that the constrained MPC control algorithm is advanced in the accuracy and stability of trajectory tracking control under high-speed obstacle avoidance.

Distributed Data-Driven Model Predictive Control for Heterogeneous Vehicular Platoon With Uncertain Dynamics

To alleviate the adverse effects of heterogeneous vehicular platoon (HVP) with uncertain dynamics, a distributed data-driven model predictive control (DDMPC) strategy is proposed in this paper. A data-driven model is established with subspace identification using the input-output (I/O) vehicle trajectory. We integrate the data-driven model with the distributed model predictive control (MPC) algorithm to optimize the HVP control. Then, a DDMPC optimal scheme is designed with a target equilibrium and a pair of initial/terminal constraints. Its recursive feasibility and exponential stability are guaranteed by an I/O-to-state stability (IOSS) Lyapunov function and an optimal sum cost function. The string stability analysis of HVP also is provided. Finally, several experiments with heterogeneous vehicular platoon demonstrate the effectiveness of the proposed DDMPC strategy.

A Novel DC Electronic Load Topology Incorporated with Model Predictive Control Approach

This paper presents a novel topology of a modified isolated single-ended-primary-inductance converter (SEPIC) with a model predictive control (MPC) approach applied to direct current (DC) electronic loads. The proposed converter uses an actual transformer rather than a coupled inductor for isolation between the source and the load. The transformer allows the proposed converter to operate at a higher switching frequency, ultimately reducing the passive components’ size. A low-power hardware prototype is developed and tested with a model predictive control algorithm under variable input voltages and load conditions. The performance of the proposed converter is demonstrated to be satisfactory under steady state, as well as sudden input voltage transients. The proposed converter utilizes a switched capacitor technique to generate alternating current in both windings of the transformer. As the coupled inductor is eliminated from the circuit, the problem of high voltage spikes occurring due to leakage inductances is also eliminated for the proposed converter. Therefore, the proposed converter can be used for isolated medium power applications. The experimental results show that the efficiency of the proposed converter reached 96%. The MPC allows this converter’s DC voltage level to remain stable even as the input voltage and output terminal load change. Lastly, this converter with an MPC approach can be applied to different DC electronic loads, improving DC power quality and DC electronic load life.

Robustness analysis of gain‐scheduled Model Predictive Control for Linear Parameter Varying systems: An Integral Quadratic Constraints approach

AbstractIn this paper, we evaluate the robustness qualities of Model Predictive Control (MPC) algorithms applied for Linear Parameter Varying (LPV) systems. Specifically, we analyze gain‐scheduled LPV MPC schemes, that is, those that use model predictions based on the LPV scheduling variables available at each sampling instant. Accordingly, we extend previous results on finite‐horizon robustness analysis of linear time‐variant (LTV) systems, employing Integral Quadratic Constraints (IQCs) to describe the input‐output behavior of prediction uncertainties. We provide two main novelties in our formulation: (i) we propose a parameter‐dependent Karush–Kuhn–Tucker (KKT) inequality to describe the existence and feasibility of the LPV MPC control inputs; and (ii) we model the uncertainties that arise due to the unavailability of the scheduling trajectory along the prediction horizon as a bounded interconnection in the form of a Linear Fractional Transformation (LFT). Accordingly, we use dissipativity arguments (‐hard IQCs) in order to compute robust induced gains of the closed‐loop system (specifically, the and ‐to‐Euclidean metrics), taking into account the MPC prediction uncertainties. We also generate the set of reachable states from a given initial condition. A benchmark example is used to illustrate the proposed analysis procedure.

Active disturbance rejection controller design for harmonic suppression in MPC optimal control based on harmonic state space modeling

SummaryThis paper proposes a harmonic suppression strategy for photovoltaic‐voltage source converters (PV‐VSC). The proposed method utilizes a harmonic state space (HSS) model‐combine model predictive control (MPC) algorithm, which provides a comprehensive representation of the global characteristics of AC/DC harmonic coupling impedance in photovoltaic systems. The HSS‐based converter model operates as a linear time‐varying periodic system, which allows for the implementation of MPC to optimize the HSS equation. This optimization yields voltage increments across various harmonic orders, providing feedforward compensation for the current controller. By incorporating feedback signals into the current control, harmonic compensation is achieved, resulting in improved power quality. Furthermore, a decoupled proportional resonance‐linear adaptive disturbance rejection control (PR‐LADRC) is employed to effectively mitigate steady‐state errors and decrease total harmonic distortion in the presence of DC‐side disturbances. This is accomplished by decoupling the disturbance observer from the controller and integrating the increment feedback signal derived from the MPC‐HSS. Finally, the efficacy of the proposed method and the analysis results are corroborated through numerical simulations.

The transition from batch to continuous manufacturing for tablet manufacturing performance comparison and control system review

The pharmaceutical industry is motivated to improve tablet productivity in order to adapt to the expanding drug market. One innovative way is the transition of the traditional batch tablet manufacturing process to a continuous process. This paper aims to review the performance of continuous tablet manufacturing processes. From the comparison of continuous to traditional batch processes, the continuous tablet manufacturing process demonstrates improvements in production flexibility, robustness, and ultimately process profitability. The continuous process is proven to mix drug particles with more evenly distributed size and less segregation resulting in tablets with better quality compared to the traditional batch process. The improvement on the continuous tablet manufacturing process requires robust control system for process automation to counteract process noise and disturbances in process feed. A well-tuned Proportional Integral Derivative (PID) control is sufficient for the process to produce a desirable product. Developing a new algorithm for model predictive control (MPC) and enhancing the control system with MPC-PID control can further improve the system performance. Overall, the transition from batch to continuous tablet manufacturing is supported by pharmaceutical companies as well as organizations like Food and Drug Administration (FDA).