To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization eff...
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To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies.
To solve the problem of large fluctuation of vehicle following distance in cooperative adaptive cruise control (CACC), a distributed model predictive control (DMPC) strategy is proposed. The idea of hierarchical contr...
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To solve the problem of large fluctuation of vehicle following distance in cooperative adaptive cruise control (CACC), a distributed model predictive control (DMPC) strategy is proposed. The idea of hierarchical control is performed to control the CACC system. The controller is divided into an upper controller and a lower controller. The upper controller calculates the expected acceleration of the vehicle according to the platooning state, and the lower controller controls the throttle and braking system pressure of the vehicle according to the expected acceleration. Firstly, the longitudinal dynamic model of vehicle platooning is established. Secondly, the objective function is designed according to the control objectives, so that the platooning can obtain the optimal control quantity at the current time. Meanwhile, the robust design is used to improve the controller performance, and the optimization of reference trajectory and the extension of feasible domain are used to improve the stability of the controller. Car-following Stability therefore can be improved. Then the lower controller is designed based on a reverse engine model and a reverse braking model. Finally, the effectiveness of the designed control strategy is verified by the co-simulation of Carsim and MATLAB/Simulink. The results show that DMPC can reduce the peak value, the standard deviation, and the root mean square of vehicle following distance error and improve the following stability.
This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-tri...
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This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.
The online computational burden and control performance are two main issues in implementing distributed model predictive control for piecewise affine systems. In the previous research, many methods have been proposed ...
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The online computational burden and control performance are two main issues in implementing distributed model predictive control for piecewise affine systems. In the previous research, many methods have been proposed to solve these issues, such as a one-step distributed model predictive control method that was proposed to reduce the heavy online computational burden. However, its control performance is limited because of the one-step prediction horizon. This article proposes an event-triggered distributed model predictive control algorithm for such systems. The online computational burden is alleviated within the distributed framework through the use of an event-triggered mechanism and a variable prediction horizon approach. These strategies not only reduce the number of optimization problems requiring online resolution and simplify them, but also allow for a balance between the control performance and the online computational burden by adjusting event-triggering thresholds. The algorithm utilizes state tubes and terminal sets that are tailored to the characteristics of the piecewise affine systems, thereby rigorously establishing the recursive feasibility of the optimization problems and the stability of the closed-loop system. The simulation results validate the effectiveness of the proposed algorithm.
This paper presents a distributed model predictive control (DMPC) scheme for non-linear continuous-time systems. The underlying distributed optimal control problem is cooperatively solved in parallel via a sensitivity...
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This paper presents a distributed model predictive control (DMPC) scheme for non-linear continuous-time systems. The underlying distributed optimal control problem is cooperatively solved in parallel via a sensitivity-based algorithm. The algorithm is fully distributed in the sense that only one neighbor-to-neighbor communication step per iteration is necessary and that all computations are performed locally. Sufficient conditions are derived for the algorithm to converge towards the central solution. Based on this result, stability is shown for the suboptimal DMPC scheme under inexact minimization with the sensitivity-based algorithm and verified with numerical simulations. In particular, stability can be guaranteed with either a suitable stopping criterion or a fixed number of algorithm iterations in each MPC sampling step, which allows for a real-time capable implementation.
This paper introduces an integrated approach for time-coordinated motion planning of multiple unmanned vehicles using distributed model predictive control (DMPC) and sequential convex programming (SCP). This approach ...
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This paper introduces an integrated approach for time-coordinated motion planning of multiple unmanned vehicles using distributed model predictive control (DMPC) and sequential convex programming (SCP). This approach employs a unified framework that integrates trajectory planning and tracking into a single optimization problem, effectively expanding the domain of attraction for the MPC controller and addressing the challenge of time-coordination among multiple vehicles. Non-uniform discrete time scales are introduced to mitigate the dimensionality of the optimization problem, thereby enhancing computational efficiency. By combining the ability of DMPC to distribute computational efforts across multiple vehicles with the iterative convexification method of SCP, our approach efficiently handles the complexities of non-linear optimization. Theoretical analysis has confirmed the feasibility and stability of the proposed method. Based on this approach, the time-coordinated sequential convex programming-based distributed model predictive control (TC-SCP-DMPC) algorithm is proposed. Numerical simulations are conducted to validate the effectiveness and efficiency of the proposed algorithm in achieving time-coordinated control of multiple unmanned vehicles.
This work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distr...
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This work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distributed MPC (DMPC) to achieve the global performance of centralized MPC (CMPC). However, control performance can be severely degraded by unreliable communication networks, for example, with denial of service (DoS) attacks. A resilient control framework is derived to address the unreliable communications in DMPC. A global system is divided into subsystems for the distributedcontrol purpose. To deal with the model uncertainties and state delays, a "min-max" DMPC algorithm is presented with a buffer to ensure resilience against DoS attacks. A quantization scheme is introduced to quantize the control information exchanged between subsystems. An iterative interaction scheme is proposed to exchange feedback control laws among subsystems. The stability of the closed-loop system under the proposed algorithm is ensured by using a Lyapunov function method. The effectiveness of the proposed DMPC is demonstrated through two simulation examples.
This paper investigates the formation tracking problem for multiple mobile robots via self-triggered distributed model predictive control (DMPC) strategy and path-parameter communication manner. To ensure the robots f...
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This paper investigates the formation tracking problem for multiple mobile robots via self-triggered distributed model predictive control (DMPC) strategy and path-parameter communication manner. To ensure the robots follow the desired formation structure along the predefined paths, we establish appropriate tracking error models that form a multi-agent system. At triggered instants, each agent exchanges a sequence of path parameters representing the robot's position, resolves the optimal control problem (OCP) and subsequently determines the open-loop phase. Different from existing coordination methodology, the proposed scheme exhibits two essential merits in environments where resources are particularly limited: (1) The tracking task of robots is achieved by designing an appropriate OCP under the DMPC scheme, and the formation task of robots is achieved through the synchronization of one-dimensional path parameters instead of the multi-dimensional state information, which demands less communication load;(2) The incorporation of the self-triggered scheduler acquires the desired control performance with less calculation time, thereby relieving the computational and communication costs. Sufficient conditions are proposed to guarantee the recursive feasibility of the OCP and the closed-loop stability. Simulation results illustrate the validity of the proposed control algorithm.
This article tackles the complex challenge of multi-vehicle motion planning by presenting the novel Sequential Convex Programming-based distributed model predictive control (SCP-DMPC) algorithm. Existing methods often...
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This article tackles the complex challenge of multi-vehicle motion planning by presenting the novel Sequential Convex Programming-based distributed model predictive control (SCP-DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi-vehicle motion planning, frequently compromising on computational efficiency. SCP-DMPC innovatively combines the predictivecontrol of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three-stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic-based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle-laden formation transition, SCP-DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real-time unmanned vehicle applications, suggesting new directions for future research and development in the field.
This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy fo...
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This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy for Leader-Follower formation control scheme in a group of multiple perturbed Wheeled Mobile Robotics (WMRs) with the consideration of tracking performance in not only the position but also the orientation, as well as the distance between the center and head in each WMR. Furthermore, the relation between formation control objective and non-holonomic property in each agent is also discussed. For the purpose of achieving the desired formation, according to trajectory of leader WMR, the barycentric of the formation requirement is known as corresponding virtual followers, and a distributed tube-MPC scheme is applied to each follower WMR for tracking a reference trajectory with not only the position but also its orientation. In addition, the stability and the performance tracking of multiple perturbed WMRs are investigated by employing Lyapunov stability theory with the indirect comparison method to be implemented by pointing out precisely the proposed terminal controller and the equivalent terminal region. Comprehensive simulation results in several scenarios demonstrate the validity of the proposed control scheme.
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