Event-triggered Model Predictive Control Research Articles

Comparison of Two-Wheel and Four-Wheel Steering Using Event-Triggered Predictive Motion Control and Scale Vehicles

Abstract This study compares the trajectory tracking performance of two- and four-wheel steering systems, especially under normal driving conditions. Specifically, the lateral motion is controlled by an event-triggered model predictive control (MPC), which activates either when consecutive control steps surpass the predictive horizon or when tracking error exceeds a predetermined lateral offset. Using a modified 1/10th scale Tamiya TT-02 RC car as a test platform, the tracking performance of both two- and four-wheel systems are evaluated. Results from the experiments highlight the better tracking performance of the four-wheel steering system over the traditional two-wheel systems and demonstrate the benefit of using event-triggered MPC for lateral motion control even under normal driving conditions, contrary to common belief that four-wheel steering systems are beneficial only in tight steering maneuvers.

Networked Output-Feedback MPC: A Bounded Dynamic Variable and Time-Varying Threshold-Dependent Event-Based Approach.

The event-triggered model predictive control (MPC) problem is addressed for polytopic uncertain systems. A new dynamic event-triggered mechanism (DETM) with a bounded dynamic variable and a time-varying threshold is proposed to manage measurement data packet releases. The dynamic output-feedback MPC issue is detailed as a "min-max" optimization problem (OP) with an objective function over an infinite horizon, where the hard constraint on the predictive control is required. By applying a Lyapunov-like function containing the bounded dynamic variable, an auxiliary OP constrained by several matrix inequalities is proposed, and the design methods of the output-feedback gains are provided if this auxiliary OP is feasible. The designed MPC controller ensures that the closed-loop system is input-to-state practically stable. Two examples including an event-triggered DC motor are given to illustrate the validity of the developed MPC algorithm. Simulation results verify that the proposed DETM has advantages over some existing triggering mechanisms in decreasing the consumption of resources while meeting the required performance.

Reinforcement Learning-Based Event-Triggered Active-Battery-Cell-Balancing Control for Electric Vehicle Range Extension

Optimal control techniques such as model predictive control (MPC) have been widely studied and successfully applied across a diverse field of applications. However, the large computational requirements for these methods result in a significant challenge for embedded applications. While event-triggered MPC (eMPC) is one solution that could address this issue by taking advantage of the prediction horizon, one obstacle that arises with this approach is that the event-trigger policy is complex to design to fulfill both throughput and control performance requirements. To address this challenge, this paper proposes to design the event trigger by training a deep Q-network reinforcement learning agent (RLeMPC) to learn the optimal event-trigger policy. This control technique was applied to an active-cell-balancing controller for the range extension of an electric vehicle battery. Simulation results with MPC, eMPC, and RLeMPC control policies are presented along with a discussion of the challenges of implementing RLeMPC.

Event-Triggered Deep Learning Control of Quadrotors for Trajectory Tracking

This paper proposes an event-triggered deep learning control strategy to achieve real-time trajectory tracking control for quadrotors. In the training data collection phase, the event-triggered model predictive control (MPC) method is applied to the quadrotor in the simulation environment to generate training data. Then, a deep neural network (DNN) controller is trained to approximate the optimal control policy of the event-triggered MPC. To further save computing resources of on-board processor, the event-triggered mechanism is incorporated with the DNN controller, and the dual-mode approach is employed in it. Finally, simulation and experimental results show that the proposed controller can ensure almost similar trajectory tracking performance to the event-triggered MPC controller while requiring a lower control computation cost.

Formation control of UAV–USV based on distributed event-triggered adaptive MPC with virtual trajectory restriction

This paper studies the formation and trajectory tracking problem of unmanned aerial vehicle-unmanned surface vehicle (UAV–USV). A distributed event-triggered adaptive model predictive control (DEAMPC) method is constructed for the formation control by using information of the adjacent vehicles. First, the preset formation equation and parameter change are incorporated into the construction process of the cost function to achieve the formation control. Second, in order to reduce the calculation times of the optimization problem, an event-triggered mechanism is proposed. Finally, the virtual trajectory restriction is designed to deal with the condition of interrupted communication network. Moreover, some simulation results are given to illustrate the advantages and effectiveness of all the above methods.

Disturbance-observer-based event-triggered model predictive control of nonlinear input-affine systems

This paper proposes a novel disturbance-observer-based event-triggered model predictive control (DEMPC) framework for a class of nonlinear input-affine systems with state and control input constraints as well as unmatched disturbances to simultaneously enhance the robustness of standard MPC and reduce computational resource utilization. First, a nonlinear disturbance observer is designed to compensate for disturbances actively. Next, a constant-threshold-type event-triggering mechanism (CTETM) is designed in terms of the state prediction deviation caused by the remaining disturbances. Subsequently, a DEMPC algorithm is constructed using the disturbance estimation information, the CTETM, and the so-called dual-mode scheme. Furthermore, rigorous theoretical analysis is provided, involving robust constraint fulfillment, Zeno phenomenon prevention, recursive feasibility, and stability. Particularly for systems with only matched disturbances, it is proven that continuous disturbance compensation would result in a possibly lower triggering frequency and enhanced control performance for the DEMPC compared to conventional EMPC without introducing additional conservatism into the state constraints. In the end, simulation studies are performed to illustrate the effectiveness of the proposed framework.

Robust model predictive control for perturbed nonlinear systems via an error differential-integral based event-triggered approach

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness.

Adaptive event-triggered fuzzy MPC for unknown networked IT-2 T-S fuzzy systems

Adaptive event-triggered fuzzy MPC for unknown networked IT-2 T-S fuzzy systems

Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving

Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving

Event-Triggered Robust MPC with Terminal Inequality Constraints: A Data-Driven Approach

Event-Triggered Robust MPC with Terminal Inequality Constraints: A Data-Driven Approach

Dynamic Event-Triggered Model Predictive Control Under Channel Fading and Denial-of-Service Attacks

Dynamic Event-Triggered Model Predictive Control Under Channel Fading and Denial-of-Service Attacks

An Error Quasi Quadratic Differential Based Event-triggered MPC for Continuous Perturbed Nonlinear Systems

An Error Quasi Quadratic Differential Based Event-triggered MPC for Continuous Perturbed Nonlinear Systems

Resilient Event-Triggered MPC for Load Frequency Regulation With Wind Turbines Under False Data Injection Attacks

Resilient Event-Triggered MPC for Load Frequency Regulation With Wind Turbines Under False Data Injection Attacks

Dynamic Event-Triggered Model Predictive Control With Guaranteed Rigorous Satisfaction of Probabilistic Path Constraints

Dynamic Event-Triggered Model Predictive Control With Guaranteed Rigorous Satisfaction of Probabilistic Path Constraints

Event-Triggered Model Predictive Control for the Inverter of a Grid-Connected Microgrid With a Battery-Supercapacitor HESS

Event-Triggered Model Predictive Control for the Inverter of a Grid-Connected Microgrid With a Battery-Supercapacitor HESS

Periodic Event-Triggered MPC for Continuous-Time Nonlinear Systems With Bounded Disturbances

This paper is concerned with periodic event-triggering laws in robust model predictive control (MPC) for continuous-time constrained nonlinear systems. The online optimal control problem solved at triggering times is introduced. A periodic static event-triggering condition, in which a fixed sampling time interval plays an important role in avoiding Zeno behavior, is presented to alleviate continuous checking of event detections. Then a periodic dynamic event-triggering condition is investigated to further enlarge the minimal inter-execution time. Single-mode MPC with a prediction horizon larger than the control one is considered. Sufficient conditions of recursive feasibility for the online optimal control problem are derived. In order to relax the sufficient condition of stability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ultimately Boundedness</i> properties are utilized in stability analysis. Finally, numerical simulation is provided to demonstrate the effectiveness of the proposed methods.

Event-triggered model predictive control for series–series resonant ICPT systems in electric vehicles: A data-driven modeling method

This paper investigates the event-triggered model predictive control (MPC) for the series–series (SS) resonant inductive coupling power transfer (ICPT) system in electric vehicles (EVs). Different from most existing literature in ICPT systems, a data-driven modeling approach based on input–output data is proposed to describe the system dynamics and achieve constant voltage output in the presence of load variations. In the traditional MPC control strategies, the optimal control input should be calculated at each time instant to achieve the desired output voltage, which causes great computational burden. To tackle this issue, an event-triggered MPC mechanism is therefore developed to effectively alleviate the computational burden, which will generate the optimal control input only when the norm of the state error exceeds a predefined threshold. The effectiveness and reliability of the proposed event-triggered MPC control strategy are successfully verified by the experimental results.

Event-triggered model predictive tracking control of aero-engine with varying prediction horizon

Aero-engine is a complex thermal-mechanical system with strong nonlinearity, uncertainty, and time variation. Thus, it is crucial to design an effective controller for such a complex system to obtain the desired performances of the aero-engine. In recent years, model predictive control (MPC) has shown great potential in dealing with control problems with complex constraints of multi-variable systems, which has been applied to aero-engine control, achieving good results. Furthermore, the MPC strategy using an event-driven mechanism is good at balancing system resources and ensuring system control performances. In this paper, the problem of event-triggered MPC for aero-engine systems with bounded disturbances is studied. Firstly, an event-triggered strategy with a dynamic forced-trigger mechanism is proposed. Then, an MPC algorithm based on an event-triggered mechanism is designed. Finally, an application to the JT9D aero-engine model provided by T-MATS verifies the effectiveness of the designed algorithm. It is shown that the calculation load is significantly reduced, which proves the superiority of this method.

Multiple Autonomous Underwater Vehicle Formation Obstacle Avoidance Control Using Event-Triggered Model Predictive Control

In this paper, multiple autonomous underwater vehicle (multi-AUV) formation control with obstacle avoidance ability in 3D complex underwater environments based on an event-triggered model predictive control (EMPC) is proposed. Firstly, multi-AUV motion model systems are developed. The navigation reference trajectory of the follower AUVs can be obtained using a multi-AUV relative motion model. Secondly, in order to overcome the speed jump and obstacle avoidance problem in multi-AUV systems, compatibility constraints are presented in MPC that limit the uncertainty deviation of each AUV. The event-triggered mechanism (ET) is designed to decrease the computational load, which is based on the error between the optimal predicted and current state of the AUV. Finally, the effectiveness and superiority of the proposed algorithm are confirmed via simulation and compared with those of other algorithms.

Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties

In order to enhance the performance of disturbance rejection in AUV’s path tracking, this paper proposes a novel tube-based event-triggered path-tracking strategy. The proposed tracking strategy consists of a speed control law and an event-triggered tube model predictive control (tube MPC) scheme. Firstly, the speed control law using linear model predictive control (LMPC) technology is obtained to converge the nominal path-tracking deviation. Secondly, the event-triggered tube MPC scheme is used to calculate the optimal control input, which can enhance the performance of disturbance rejection. Considering the nonlinear hydrodynamic characteristics of AUV, a linear matrix inequality (LMI) is formulated to obtain tight constraints on the AUV and the feedback matrix. Moreover, to enhance real-time performance, tight constraints and the feedback matrix are all calculated offline. An event-triggering mechanism is used. When the surge speed change command does not exceed the upper bound, adaptive tight constraints are obtained. Finally, numerical simulation results show that the proposed tube-based event-triggered path-tracking strategy can enhance the performance of disturbance rejection and ensure good real-time performance.