基于隐马尔可夫的网络控制系统稳定性分析

讨论了一类具有随机通信时延的网络控制系统的建模及稳定性分析,其中网络诱导时延受控于一概率分布未知的马尔可夫链,其概率分布可通过Baum-Welch算法计算.基于隐马尔町夫模型理论,将采用状态反馈的闭环网络控制系统建模成跳变线性系统,给出了这类网络控制系统随机稳定的允分条件,并将状态反馈控制器的求解问题转化为线性矩阵不等式的解的问题.最后,通过一个仿真算例说明了上述判定系统稳定性条件的有效性.     

Novel delay-partitioning stabilization approach for networked control system via Wirtinger-based inequalities

This paper studies the problems of stability analysis and state feedback stabilization for networked control system. By developing a novel delay-partitioning approach, the information on both the range of network-induced delay and the maximum number of consecutive data packet dropouts can be taken into full consideration. Various augmented Lyapunov–Krasovskii functionals (LKFs) with triple-integral terms are constructed for the two delay subintervals. Moreover, the Wirtinger-based inequalities in combination with an improved reciprocal convexity are utilized to estimate the derivatives of LKFs more accurately. The proposed approaches have improved the stability conditions without increasing much computational complexity. Based on the obtained stability criterion, a stabilization controller design approach is also given. Finally, four numerical examples are presented to illustrate the effectiveness and outperformance of the proposed approaches.     

Asynchronous update based networked predictive control system using a novel proactive compensation strategy

Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.     

Improved stabilization method for networked control systems with variable transmission delays and packet dropout

This paper investigates the problem of stability analysis and stabilization for networked control systems with the network-induced delay and data dropout. In order to obtain less conservative results, a novel augmented Lyapunov–Krasovskii functional is introduced and new free-weighting matrices are employed to make some extra degrees of freedom in the sufficient stabilizability condition. The gain of the memoryless state-feedback controller is computed by solving a set of linear matrix inequalities (LMIs). Illustrative examples are given to verify the applicability and outperformance of the proposed method compared to the existing approaches in the literature.     

A packet-based dual-rate PID control strategy for a slow-rate sensing Networked Control System

This paper introduces a packet-based dual-rate control strategy to face time-varying network-induced delays, packet dropouts and packet disorder in a Networked Control System. Slow-rate sensing enables to achieve energy saving and to avoid packet disorder. Fast-rate actuation makes reaching the desired control performance possible. The dual-rate PID controller is split into two parts: a slow-rate PI controller located at the remote side (with no permanent communication to the plant) and a fast-rate PD controller located at the local side. The remote side also includes a prediction stage in order to generate the packet of future, estimated slow-rate control actions. These actions are sent to the local side and converted to fast-rate ones to be used when a packet does not arrive at this side due to the network-induced delay or due to occurring dropouts. The proposed control solution is able to approximately reach the nominal (no-delay, no-dropout) performance despite the existence of time-varying delays and packet dropouts. Control system stability is ensured in terms of probabilistic Linear Matrix Inequalities (LMIs). Via real-time control for a Cartesian robot, results clearly reveal the superiority of the control solution compared to a previous proposal by authors.     

Robust control of saturating systems with Markovian packet dropouts under distributed MPC

In this work, a robust control methodology is presented for saturating systems with packet dropouts under distributed model predictive control framework. The sequence of time instants when data dropout happens is modeled by a Markov chain. A packet dropout compensation strategy and an augmented Markov jump linear model are considered simultaneously. To design distributed model predictive controllers, the entire system is decomposed into coupled subsystems. Considering the influences of neighbor subsystems, a distributed predictive control synthesis involving packet dropouts and Markovian probabilities is developed by minimizing the worst-case performance index at each time instant. The input saturation constraints are also incorporated into the robust controller design under distributed model predictive control framework. Furthermore, both the recursive feasibility of the proposed robust control under distributed model predictive control and the closed-loop mean-square stability are proved. To show the effectiveness, the proposed methodology is validated by simulations on a continuous stirred tank reactor process and a DC control system.     

Development of a decentralized multi-axis synchronous control approach for real-time networks

The message scheduling and the network-induced delays of real-time networks, together with the different inertias and disturbances in different axes, make the synchronous control of the real-time network-based systems quite challenging. To address this challenge, a decentralized multi-axis synchronous control approach is developed in this paper. Due to the limitations of message scheduling and network bandwidth, error of the position synchronization is firstly defined in the proposed control approach as a subset of preceding-axis pairs. Then, a motion message estimator is designed to reduce the effect of network delays. It is proven that position and synchronization errors asymptotically converge to zero in the proposed controller with the delay compensation. Finally, simulation and experimental results show that the developed control approach can achieve the good position synchronization performance for the multi-axis motion over the real-time network.     

Stability analysis and compensation of network-induced delays in communication-based power system control: A survey

This survey is to summarize and compare existing and recently emerging approaches for the analysis and compensation of the effects of network-induced delays on the stability and performance of communication-based power control systems. Several important communication-based power control systems are briefly introduced. The deterministic and stochastic methodologies of analyzing the impacts of network-induced delays on the stability of the communication-based power control systems are summarized and compared. A variety of control approaches are reviewed and compared for mitigating the effects of network-induced delays, depending on several design requirements, such as model dependence and design difficulty. The summary and comparison of these control approaches in this survey provide researchers and utilities valuable guidance for designing advanced communication-based power control systems in the future.     

Model predictive control of non-linear systems over networks with data quantization and packet loss

This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.     

Stabilizing model predictive control for constrained nonlinear distributed delay systems

In this paper, a model predictive control scheme with guaranteed closed-loop asymptotic stability is proposed for a class of constrained nonlinear time-delay systems with discrete and distributed delays. A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability. To determine the terminal cost, a locally asymptotically stabilizing controller is designed and an appropriate Lyapunov-Krasoskii functional of the locally stabilized system is employed as the terminal cost. Furthermore, an invariant set for locally stabilized system which is established by using the Razumikhin Theorem is used as the terminal region. Simple conditions are derived to obtain terminal cost and terminal region in terms of Bilinear Matrix Inequalities. The method is illustrated by a numerical example.     

Constrained tracking control for nonlinear systems

This paper proposes a tracking control strategy for nonlinear systems without needing a prior knowledge of the reference trajectory. The proposed method consists of a set of local controllers with appropriate overlaps in their stability regions and an on-line switching strategy which implements these controllers and uses some augmented intermediate controllers to ensure steering the system states to the desired set points without needing to redesign the controller for each value of set point changes. The proposed approach provides smooth transient responses despite switching among the local controllers. It should be mentioned that the stability regions of the proposed controllers could be estimated off-line for a range of set-point changes. The efficiencies of the proposed algorithm are illustrated via two example simulations.     

Distributed fault-tolerant model predictive control for intermittent fault: A cooperative way

For intermittent actuator faults of large-scale system, a cooperative distributed fault-tolerant model predictive control (DFTMPC) is presented. The actuator plug and play strategy is adopted in the interconnected systems with physical coupling making fault estimation and controller redesign unnecessary. The actuator plug and play process is modeled as a distributed switching model, and there a theoretical stability analysis is provided with switching form of model predictive control (MPC) cost functions. The novel cooperative distributed fault-tolerant performance index is raised in a global view for distributed model predictive control. A simulation example is taken to show the e?ectiveness of the proposed method.     

Reachable set estimation for Takagi-Sugeno fuzzy systems against unknown output delays with application to tracking control of AUVs

In this paper, we address the problem of reachable set estimation for continuous-time Takagi-Sugeno (T-S) fuzzy systems subject to unknown output delays. Based on the reachable set concept, a new controller design method is also discussed for such systems. An effective method is developed to attenuate the negative impact from the unknown output delays, which likely degrade the performance/stability of systems. First, an augmented fuzzy observer is proposed to capacitate a synchronous estimation for the system state and the disturbance term owing to the unknown output delays, which ensures that the reachable set of the estimation error is limited via the intersection operation of ellipsoids. Then, a compensation technique is employed to eliminate the influence on the system performance stemmed from the unknown output delays. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of autonomous underwater vehicles.     

Robust output feedback distributed model predictive control of networked systems with communication delays in the presence of disturbance

In this work, an output feedback cooperative distributed model predictive control is developed for a class of networked systems composed of interacting subsystems interconnected through their states, in which it handles bounded disturbances and time varying communication delays. A distributed buffer based prediction strategy is used to compensate bounded delays and predict those states, which are coupled between subsystems that their actual values may not available due to delays. In the design of robust distributed model predictive control, distributed moving horizon estimation is employed so that convergence and boundedness of the estimation error are ensured. Furthermore, robust exponential stability of the closed loop system is established. The effectiveness of the proposed method is illustrated using two interconnected continuous stirred tank reactors.     

Acceleration tracking control combining adaptive control and off-line compensators for six-degree-of-freedom electro-hydraulic shaking tables

An electro-hydraulic shaking table (EHST) is an essential experimental facility to simulate in real-time actual vibration situations. An adaptive controller combined with off-line compensators is proposed to improve the acceleration frequency bandwidth and tracking accuracy of a six-degree-of-freedom (6-DOF) EHST. A servo controller has been employed to implement acceleration closed-loop and coordinate control of the 6-DOF EHST. A recursive extended least-squares algorithm is employed to identify acceleration closed-loop transfer functions and a zero-phase-error tracking controller is used to design off-line inverse model compensators using the identified transfer functions. However, the off-line compensators cannot compensate in real-time varying dynamics of the 6-DOF EHST; so an online adaptive controller with a least-mean-squares (LMS) algorithm based on a delay compensator is employed. The proposed controller combines advantages of the off-line compensators and the online adaptive controller, which guarantees both a fast rate of convergence of the LMS algorithm and high-fidelity acceleration tracking accuracy of the 6-DOF EHST. Some experimental studies have been conducted on a 6-DOF EHST and experimental results show that acceleration tracking control performances, including the rate of convergence of the LMS algorithm and acceleration tracking accuracy, have been improved compared to a conventional three-variable controller and adaptive controllers.     

Improved stability and stabilization design for networked control systems using new quadruple-integral functionals

This paper investigates stability analysis and stabilization for networked control systems. By a refined delay decomposition approach, slightly different Lyapunov–Krasovskii functionals (LKFs) with quadruple-integral terms and augmented vectors containing triple-integral forms of state are constructed. New integral inequalities are proposed to estimate the cross terms from derivatives of the LKFs, which can be proved to offer tighter bounds than what the Jensen one produces theoretically. Moreover, the non-strictly proper rational functions in deriving process are fully handled via reciprocally convex approach. A state feedback controller design approach is also developed. Numerical examples and applications to practical power and oscillator systems demonstrate the superiority of the proposed criteria in conservatism reduction compared to some existing ones.     

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC.     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection.     

Simulation and control of an electro-hydraulic actuated clutch

The basic function of any type of automotive transmission is to transfer the engine torque to the vehicle with the desired ratio smoothly and efficiently and the most common control devices inside the transmission are clutches and hydraulic pistons. The automatic control of the clutch engagement plays a crucial role in Automatic Manual Transmission (AMT) vehicles, being seen as an increasingly important enabling technology for the automotive industry. It has a major role in automatic gear shifting and traction control for improved safety, drivability and comfort and, at the same time, for fuel economy. In this paper, a model for a wet clutch actuated by an electro-hydraulic valve used by Volkswagen for automatic transmissions is presented. Starting from the developed model, a simulator was implemented in Matlab/Simulink and the model was validated against data obtained from a test-bench provided by Continental Automotive Romania, which includes the Volkswagen wet clutch actuated by the electro-hydraulic valve. Then, a predictive control strategy is applied to the model of the electro-hydraulic actuated clutch with the aims of controlling the clutch piston displacement and decreasing the influence of the network-induced delays on the control performances. The simulation results obtained with the proposed method are compared with the ones obtained with different networked controllers and it is shown that the strategy proposed in this paper can indeed improve the performances of the control system.