With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the...
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With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the interference of human-driven vehicles on multi-platoon stability and the following strategy of multi-platoon leader in mixed traffic. In this paper, a robust distributed model predictive control method for multi-platoon leader in mixed traffic is proposed to reduce the impact of human-driven vehicles on multi-platoon control performance. The following control strategy of multi-platoon leader is proposed firstly, which flexibly determines the following control targets according to the states of leader and HDV to avoid unnecessary frequent acceleration and deceleration. Then, the robustmodel prediction controller of multi-platoon leader is designed, where the states of sub-platoon leader are added to the objective function in the nominal system optimization problem to reduce the states change of the following vehicles under the influence of HDV from both forward and backward traffic. Furthermore, the auxiliary control law is designed to eliminate the error between the actual states and the nominal states to achieve the suppression of HDV interference. The simulation results show that the multi-platoon leader following control strategy can effectively reduce the speed variation of the multi-platoon to suppress the impact of HDV motion uncertainty on multi-platoon. Moreover, compared with the robustmodel prediction method of single-platoon leader without considering the state of the rear vehicle, the proposed method can reduce the control errors and improve the stability of multi-platoon.
The parallel structure is one of the basic system architectures found in process networks. In order to achieve robustcontrol of complex process networks, it is necessary to formulate control strategies that specifica...
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The parallel structure is one of the basic system architectures found in process networks. In order to achieve robustcontrol of complex process networks, it is necessary to formulate control strategies that specifically accommodate the characteristics of such parallel systems. In this paper, the competitive coupling and competitive constraints in parallel systems are initially defined. A novel robust distributed model predictive control algorithm is then developed for such parallel systems which deals explicitly with competitive couplings, competitive constraints and uncertainties. The Lyapunov Method is used for the theoretical analysis which produces tractable linear matrix inequalities (LMI). Two simulation studies and an experimental trial are provided to validate the effectiveness of the proposed approach. These consider control of 40 user and 100 user gas boiler heating systems as well as control of two continuous stirred tank reactors (CSTRs) which are connected in parallel. (C) 2019 Elsevier Ltd. All rights reserved.
The parallel structure is one of the basic system architectures found in process networks. This paper formulates control strategies for such parallel systems when the states are unmeasured. The competitive coupling an...
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The parallel structure is one of the basic system architectures found in process networks. This paper formulates control strategies for such parallel systems when the states are unmeasured. The competitive coupling and competitive constraints are addressed in the control design. A distributed buffer and pre-estimator are proposed to solve problems relating to coupling and timely communication whilst a distributed moving horizon estimator is employed to further improve the estimation accuracy in the presence of the constraints. An output feedback robust distributed model predictive control algorithm is then developed for such parallel systems. The Lyapunov method is used for the theoretical analysis which produces tractable linear matrix inequalities (LMI). Simulations and experimental results are provided to validate the effectiveness of the proposed approach.
Reliability in of importance in load frequency control (LFC) in modern power generation, In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, and generation rate constraints (...
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ISBN:
(纸本)9781665403450
Reliability in of importance in load frequency control (LFC) in modern power generation, In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, and generation rate constraints (GRC), are the major nonlinear factors on the control scheme that affect the dynamic response of the system. In this paper, two-area hydro-thermal interconnected power system (IPS) is used to develop an adaptive modelpredictivecontrol (AMPC) as a controller scheme for LFC scheme and compared with both robust distributed model predictive control (RDMPC) and proportional integral derivative (PID) controller, using MATLAB/Simulink, result shows that AMPC shows robustness over RDMPC and PID when compared in frequency deviation, area control error (ACE) and tie-line active power deviations.
Reliable Load frequency control (LFC) is crucial to the operation and design of modern electric power systems. However, the power systems are always subject to uncertainties and external disturbances. Considering the ...
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Reliable Load frequency control (LFC) is crucial to the operation and design of modern electric power systems. However, the power systems are always subject to uncertainties and external disturbances. Considering the LFC problem of a multi-area interconnected power system, this paper presents a robust distributed model predictive control (RDMPC) based on linear matrix inequalities. The proposed algorithm solves a series of local convex optimization problems to minimize an attractive range for a robust performance objective by using a time-varying state-feedback controller for each control area. The scheme incorporates the two critical nonlinear constraints, e.g., the generation rate constraint (GRC) and the valve limit, into convex optimization problems. Furthermore, the algorithm explores the use of an expanded group of adjustable parameters in LMI to transform an upper bound into an attractive range for reducing conservativeness. Good performance and robustness are obtained in the presence of power system dynamic uncertainties. (C) 2016 Elsevier Ltd. All rights reserved.
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