Partially Decentralized Controller Design via Model Predictive Control

An expansion procedure to design partially decentralized controllers via model predictive control is proposed in this paper. Partially decentralized control is a control structure that lies between a fully decentralized structure and a fully centralized one, and has the advantage of achieving comparable performance as a fully centralized controller but with simpler structure. The proposed method follows the expansion method proposed in a previous paper where internal model control (IMC) was used to design controllers for non-square subsystems. The method requires computing the pseudo-inverse of a non-square matrix via pseudo-inverse factors. Instead, the proposed method uses dynamic matrix control (DMC) to design PID controllers for non-square subsystems without using additional factors. The effectiveness of the proposed method is demonstrated on several chemical examples. Simulation results show that the proposed method is simple and can achieve better performance.

MODAL SYNTHESIS METHOD FOR NORM COMPUTATION OF H_∞ DECENTRALIZED CONTROL SYSTEMS (II)

When using H_∞ techniques to design decentralized controllers for large systems, the whole system is divided into subsystems, which are analysed using H_∞ control theory before being recombined. An analogy was established with substructural analysis in structural mechanics, in which H_∞ decentralized control theory corresponds to substructural modal synthesis theory so that the optimal H_∞ norm of the whole system corresponds to the fundamental vibration frequency of the whole structure. Hence, modal synthesis methodology and the extended Wittrick_Williams algorithm were transplanted from structural mechanics to compute the optimal H_∞ norm of the control system. The orthogonality and the expansion theorem of eigenfunctions of the subsystems H_∞ control are presented in part (Ⅰ) of the paper. The modal synthesis method for computation of the optimal H_∞ norm of decentralized control systems and numerical examples are presented in part (Ⅱ).

ROBUST SLIDING MODE DECENTRALIZED CONTROL FOR A CLASS OF NONLINEAR INTERCONNECTED LARGE-SCALE SYSTEM WITH NEURAL NETWORKS

A new decentralized robust control method is discussed for a class of nonlinear interconnected largescale system with unknown bounded disturbance and unknown nonlinear function term. A decentralized control law is proposed which combines the approximation method of neural network with sliding mode control. The decentralized controller consists of an equivalent controller and an adaptive sliding mode controller. The sliding mode controller is a robust controller used to reduce the track error of the control system. The neural networks are used to approximate the unknown nonlinear functions, meanwhile the approximation errors of the neural networks are applied to the weight value updated law to improve performance of the system. Finally, an example demonstrates the availability of the decentralized control method.

Key Technologies for Decentralized Control Platform with Dynamic Evolution for Railway Transportation System

Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of the control center migration, the principle and technology of system self-monitoring, the principle and technology of the switch-mode of remote control station, the information transmission technology, and the data consistency technology. These key technologies have shown a series of advanced characteristics for decentralized control platform.

ON DECENTRALIZED OBSERVATION AND DECENTRALIZED CONTROL OF LARGE SCALE SYSTEMS

In this paper, the problem of decentralized observation and decentralized control of linear, time-invariant large scale systems is studied. Based on conventional observer theory, the necessary and sufficient conditions for existence of completely decentralized observer are obtained. Moreover, the necessary and sufficient condition is given for stabilization of large scale systems by local output with state of the subsystem estimated through the completely decentralized observer.

Augmented Lagrangian Genetic Algorithm Based Decentralized Control Configuration Design for Fluid Catalytic Cracking Units

In this work, three decentralized control configuration designs—independent, sequential and simultaneous designs—were used in multivariable feedback configurations for PI control of the riser and regenerator temperatures of FCCU in order to compare their performances. Control design was formulated as optimization problem to minimize infinity norm of weighted sensitivity functions subject to μ-interaction measure bound on diagonal complementary functions of the closed loop system. The optimization problem was solved using augmented Lagrangian genetic algorithm. Simulation results show that simultaneous and independent designs give good response with less overshoot and with no oscillation. Bound on μ-interaction measure is satisfied for both designs meaning that their nominal stabilities are guaranteed;however, it is marginal for simultaneous design. Simultaneous design outperforms independent design in term of robust performance while independent design gives the best performance in terms of robust stability. Sequential design gives the worst performance out of the three designs.

Combined centralized and decentralized control for high-precision spacecraft

The improved structural filter combined with Positive Position Feedback(PPF) controller is investigated for high-precision attitude control of flexible spacecraft which consists of rigid central body and flexible appendages.PPF controller is adopted for high frequency vibration suppression,while the improved structural filter is used for suppression of low frequency vibration.After introducing PPF controller,the vibration frequencies are changed.In view of the frequency uncertainties,an improved structural filter is designed,and the stability study for the centralized control system is conducted.The simulation results show that the performance of spacecraft control system is improved,and the control inputs remain unchanged.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

Decentralized control in active distribution grids via supervised and reinforcement learning

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

Decentralized control design and implementation for magnetic levitation systems with extended state observer

This paper focuses on control design and synthesize for a class of magnetic levitation systems,which have a decentralized control for each suspension point.Due to the existence of mechanical coupling among four suspension points,large modeling uncertainties,unpredictable disturbances during the operation,and measurement noises,becomes challenging.To estimate and compensate for the effects of lumped uncertainties,this study employs the extended state observer(ESO)in conjunction with active disturbance rejection control(ADRC).Specifically,a novel ESO is proposed that utilizes output signals and their derivatives to estimate the lumped uncertainties more accurately,which simplifies the convergence proof conditions and has well engineering performance.This article is written in honor of B.M.Chen on the occasion of his 60th birthday.Specifically,this paper is inspired by his pioneering work on composite nonlinear feedback,which combines linear feedback and nonlinear compensator to enhance system performance Chen et al.(IEEE Trans Autom Control,40:427-439,2003).

DECENTRALIZED ADAPTIVE CONTROL FOR LINEAR TIME INVARIENT SYSTEMS WITH FIRST ORDER INTERCONNECTIONS

A new decentralized adaptive control scheme is presented for linear time invariant systems with first order interconnections. The proposed control scheme with “proportional plus integral” terms is used to improve the convergence rate and the ultimate bound of the tracking error. It is important to note that the adaptive scheme uses lower adaptive gains and smaller control inputs to avoid input saturation and oscillatory behavior. Simulation results are illustrated for controlling a dual inverted pendulum and a multivariable turbofan engine using the proposed adaptive scheme. These simulations validate out conclusions.

Decentralized model reference adaptive sliding mode control based on fuzzy model

A new design scheme of decentralized model reference adaptive sliding mode controller for a class of MIMO nonlinear systems with the high-order interconnections is propcsed. The design is based on the universal approximation capability of the Takagi - Seguno （T-S） fuzzy systems. Motivated by the principle of certainty equivalenteontrol, a decentralized adaptive controller is designed to achieve the tracking objective without computafion of the T-S fuzz ymodel. The approach does not require the upper bound of the uncertainty term to be known through some adaptive estimation. By theoretical analysis, the closed-loop fuzzy control system is proven to be globally stable in the sense that all signalsinvolved are bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.

Decentralized control of two DC microgrids interconnected with tie-line

This paper examines the interconnection of two DC microgrids(MGs) with tie-line. The voltages at respective MG buses are controlled to manage the powerflow across the tie-line. Formation of such a DC MG cluster ensures higher reliability of power supply andflexibility to manage distributed energy resources and loads in the system. Two MGs consist of photovoltaic and battery units interfaced by power electronic converters. The bus voltages of two DC MGs act as an indicator for the powerflow monitoring the supply-demand balance. A decentralized control approach is proposed to control each MG and bus voltage fluctuation in an allowable range. Furthermore,a mode adaptive decentralized control approach is proposed for seamless mode transition in order to assign microgrid operation modes and for the power management of DC MGs. The effectiveness of the proposed concept is validated by simulation and experimental results.

Stability analysis and decentralized control of inverter-based ac microgrid

This work considers the problem of decentralized control of inverter-based ac micro-grid in different operation modes.The main objectives are to(i)design decentralized frequency and voltage controllers,to gather with power sharing,without information exchange between microsources(ii)design passive dynamic controllers which ensure stability of the entire microgrid system(iii)capture nonlinear,interconnected and large-scale dynamic of the micro-grid system with meshed topology as a port-Hamiltonian formulation(iv)expand the property of shifted-energy function in the context of decentralized control of ac micro-grid(v)analysis of system stability in large signal point of view.More precisely,to deal with nonlinear,interconnected and large-scale structure of micro-grid systems,the port-Hamiltonian formulation is used to capture the dynamic of micro-grid components including microsource,distribution line and load dynamics as well as interconnection controllers.Furthermore,to deal with large signal stability problem of the microgrid system in the grid-connected and islanded conditions,the shifted-Hamiltonian energy function is served as a storage function to ensure incremental passivity and stability of the microgrid system.Moreover,it is shown that the aggregating of the microgrid dynamic and the decentralized controller dynamics satisfies the incremental passivity.Finally,the effectiveness of the proposed controllers is evaluated through simulation studies.The different scenarios including grid-connected and islanded modes as well as transition between both modes are simulated.The simulation conforms that the decentralized control dynamics are suited to achieve the desired objective of frequency synchronization,voltage control and power sharing in the grid-connected and islanded modes.The simulation results demonstrate the effectiveness of the proposed control strategy.

Decentralized Sliding Mode Control for a Spacecraft Flexible Appendage Based on Finite Element Method

This paper is devoted to study the application of the decentralized sliding mode control method, which is used to reduce the vibration of large spacecraft flexible appendage. In the process of control design, the sliding surface of sliding mode control is determined by minimizing the optimal cost function, and the controller is the saturation controller. The controlled structure is subject to arbitrary, unmeasurable and uncertainty disturbance forces and initial displacement. The decentralized control method and the centralized control method are used to control vibration of the structure respectively. When the system is subjected to the initial displacement or external disturbance, the computer simulation shows that both of these control methods perform effectively, but the number of Riccati equation of the decentralized method is far smaller than that of centralized control method, especially in a large system.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Adaptive Decentralized Output-Constrained Control of Single-Bus DC Microgrids

A single-bus DC microgrid can represent a wide range of applications. Control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators(DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.

Decentralized direct adaptive neural network control for a class of interconnected systems

The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized di- rect adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local informa- tion. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

Design of decentralized robust H_∞ state feedback controller

The design of decentralized robust H_∞ state feedback controller for large-scale interconnected systems with value bounded uncertainties existing in the state, control input and interconnected matrices was investigated. Based on the bounded real lemma a sufficient condition for the existence of a decentralized robust H_∞ state feedback controller was derived. This condition is expressed as the feasibility problem of a certain nonlinear matrix inequality. The controller, which makes the closed-loop large-scale system robust stable and satisfies the given H_∞ performance, is obtained by the offered homotopy iterative linear matrix inequality method. A numerical example is given to demonstrate the effectiveness of the proposed method.

Decentralized Robust Control for Large Scale Interconnected Uncertain Systems with Time delay

For a class of interconnected time delay uncertain systems satisfing the matching conditions, the sufficient condition for decentralized stabilization feedback control laws is derived based on Lyapunov stability theorem. This condition is expressed as the solvability problem of linear matrix inequalities(LMI). It can be easily determined whether or not, it is feasible and one can easily obtain the decentralized stabilizing state feedback matrices via LMI techniques. The method overcomes the limitations of the existing algebraic Riccati equation method. The given example shows the application of the method.