Enhancing Strong Neighbor-Based Optimization for Distributed Model Predictive Control Systems

Gao, Shan; Zheng, Yongjun; Li, Shaoyuan

doi:10.3390/math6050086

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…Considering that the helicopter is far from the carrier and the environment is changing, only the motion of helicopter in a short time window of future (denoted as T w ) is planned in one step of search, assuming that the discrete time interval is ∆t, and the number of path point in one step of search (denoted as N w ) can be determined (N w = T w /∆t). Based on the ideas of model predictive control [34], this "short-term" planning strategy will last before the horizontal distance between the helicopter and the carrier is reduced to a certain value. Note that the heave motion of the carrier is not considered in this phase because it will not have an influence on the result.…”

Section: Strategy In the Approaching Phasementioning

confidence: 99%

Path Planning for Autonomous Landing of Helicopter on the Aircraft Carrier

et al. 2018

Mathematics

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Helicopters are introduced on the aircraft carrier to perform the tasks which are beyond the capability of fixed-wing aircraft. Unlike fixed-wing aircraft, the landing path of helicopters is not regulated and can be determined autonomously, and the path planning problem for autonomous landing of helicopters on the carrier is studied in this paper. To solve the problem, the returning flight is divided into two phases, that is, approaching the carrier and landing on the flight deck. The feature of each phase is depicted, and the conceptual model is built on this basis to provide a general frame and idea of solving the problem. In the established mathematical model, the path planning problem is formulated into an optimization problem, and the constraints are classified by the characteristics of the helicopter and the task requirements. The goal is to reduce the terminal position error and the impact between the helicopter and the flight deck. To obtain a reasonable landing path, a multiphase path planning algorithm with the pigeon inspired optimization (MPPIO) algorithm is proposed to adapt to the changing environment. Three experiments under different situations, that is, static carrier, only horizontal motion of carrier considered, and 3D motion of carrier considered, are conducted. The results demonstrate that the helicopters can all reach the ideal landing point with the reasonable path in different situations. The small terminal error and relatively vertical motion between the helicopter and the carrier ensure a precise and safe landing.

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Section: Strategy In the Approaching Phasementioning

confidence: 99%

Path Planning for Autonomous Landing of Helicopter on the Aircraft Carrier

et al. 2018

Mathematics

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“…Subsequent to the establishment of optimal benchmarks, adaptive [13][14][15][16][17][18][19][20] and learning controls [21][22][23][24][25][26][27][28][29][30][31][32] arose with a subsequent effort to combine the two, which makes adaptive controllers optimal, where optimization occurs after first establishing the control equation, such as by using 'approximate' dynamic programming [33]. Assuming distributed models, Reference [34] and Reference [35] used neighbor-based optimization to develop predictive controls. Other researchers [36] sought extensions into nonlinear systems with actuator failures, while Reference [37] tried to use neural networks to find the model and response in a predictive topology.…”

Section: Introductionmentioning

confidence: 99%

“…Gao et al considered a distributed model predictive controller on a class of large-scale systems, which is composed of many interacting subsystems. Each of them is controlled by an individual controller where, by integrating the steady-state calculation, the designed controller proved able to guarantee the recursive feasibility and asymptotic stability of the closed-loop system in the cases of both tracking the set point and stabilizing the system to zeroes [34]. Wu et al utilized an economic model's predictive control on a nonlinear system class with input constraints for chemical seeking to first guarantee stability, and then ensure optimality (this is the opposite approach taken in this manuscript) [57].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Interpretation Methods for Predictive Control of Mechanics

Sands

2019

Algorithms

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Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.

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“…Here, a condition that ensures the existing of a decentralized invariant set with block‐diagonal feedback control law is employed as the criterion to evaluate the strength of couplings. Furthermore, Gao et al proposed a DMPC for a linear time‐invariant system, where weak couplings are ignored in the designed DMPC predictive model and their affection is controlled within an invariant set by an additional feedback controller. The interactions considered in each subsystem‐based MPC are chosen by balancing the size of the invariant set and the complication of the information connectivity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Wei

Zheng

2019

Intl J Robust & Nonlinear

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Summary This paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.

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Enhancing Strong Neighbor-Based Optimization for Distributed Model Predictive Control Systems

Cited by 9 publications

References 39 publications

Path Planning for Autonomous Landing of Helicopter on the Aircraft Carrier

Path Planning for Autonomous Landing of Helicopter on the Aircraft Carrier

Comparison and Interpretation Methods for Predictive Control of Mechanics

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

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