A nonlinear regression model-based predictive control algorithm

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.     

Sliding mode controllers for a tempered glass furnace

This paper investigates the design of two sliding mode controllers (SMCs) applied to a tempered glass furnace system. The main objective of the proposed controllers is to regulate the glass plate temperature, the upper-wall temperature and the lower-wall temperature in the furnace to a common desired temperature. The first controller is a conventional sliding mode controller. The key step in the design of this controller is the introduction of a nonlinear transformation that maps the dynamic model of the tempered glass furnace into the generalized controller canonical form; this step facilitates the design of the sliding mode controller. The second controller is based on a state-dependent coefficient (SDC) factorization of the tempered glass furnace dynamic model. Using an SDC factorization, a simplified sliding mode controller is designed. The simulation results indicate that the two proposed control schemes work very well. Moreover, the robustness of the control schemes to changes in the system׳s parameters as well as to disturbances is investigated. In addition, a comparison of the proposed control schemes with a fuzzy PID controller is performed; the results show that the proposed SDC-based sliding mode controller gave better results.     

Real-time comparison of a number of predictive controllers

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost-function. This paper presents the newer MPC schemes such as extended predictive control (EPC) and shifted MPC as well as other well known forms. The control performance of these controllers are compared using two systems that are slow and fast reacting. The closed-loop responses are compared and the differences and similarities are explained on the basis of the structure of the control schemes. Disturbance rejection and the tracking of various setpoint trajectories are performed with good closed-loop results from all the controllers. It was found that the controllers that were specifically designed to reduce the system matrix ill-conditionality such as EPC and generalized predictive control provided better control performance when compared to other MPC methods.     

Receding horizon online optimization for torque control of gasoline engines

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.     

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.     

Integration of advanced process control and full-scale dynamic simulation

In the process control industry, multivariable model predictive controller and dynamic simulation for operator training are usually available in separate packages. It is very difficult for the operators and plant engineers to find good tools for them to get trained in multivariable advanced process control. This paper presents a system, which integrates the advanced process control and full-scale dynamic simulation. The advanced process control uses multivariable model predictive control techniques. The model used in the predictive control algorithms is generated from the dynamic simulated process. The advanced process controller can control the simulated plant directly, or through a DCS system to control the simulated plant. The combined system provides an excellent environment for training operators in process operation with multivariable advanced process control. The same environment is also very useful for engineers in designing and tuning the advanced process controllers, and in testing communication between the advanced process controller and the DCS systems, or the other type of process control systems.     

Multivariable robust adaptive sliding mode control of an industrial boiler–turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler–turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler–turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input–output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions.     

Model predictive control for systems with fast dynamics using inverse neural models

In this work, a novel model predictive control (MPC) scheme is introduced, by integrating direct and indirect neural control methodologies. The proposed approach makes use of a robust inverse radial basis function (RBF) model taking into account the applicability domain criterion, in order to provide a suitable initial starting point for the optimizer, thus helping to solve the optimization problem faster. The performance of the proposed controller is evaluated on the control of a highly nonlinear system with fast dynamics and compared with different control schemes. Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics.     

Well-conditioned model predictive control

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.     

Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process

It is well known that surface alloying quality may vary significantly with respect to process parameter variation. Thus a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output (MIMO) system has nonlinear coupling and time-varying dynamic characteristics, it is very difficult to establish an accurate process model for designing a model-based controller. Hence an adaptive fuzzy sliding-mode controller (AFSMC) which combines an adaptive rule with fuzzy and sliding-mode control is employed in this study. It has an on-line learning ability for responding to a system’s nonlinear and time-varying behaviours. Two adaptive fuzzy sliding-mode controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivitiy and geometrical variations of the work pieces. The simulation results show that good surface lapping performance is achieved by using this intelligent control strategy.     

On robust dynamic controller design

The general formulations of dynamic controllers are provided and two types of dynamic control schemes are developed. A design methodology has been synthesized in the time-domain. New suffiicient conditions are established for asymptotically stabilizing the dynamic controlled systems when the system has structured norm-bounded uncertainties in the continuous-time as well as in the discrete-time. Stability robustness is usually measured by the tolerance of plant matrix perturbations and the feedback control law in the time-domain. In an illustration, two dynamic control algorithms are implemented in an retail model of Industrial Dynamics to describe the design procedure.     

焦化鼓风机系统智能控制策略研究及应用

针对焦化鼓风机系统具有非线性时变、多变量、强耦合及存在随机干扰的特点,通过采用基于最近邻聚类方法的RBF神经网络快速学习算法,实时在线辨识,建立被控对象的精确逆模型并用于控制,实现了将具有强耦合特性的多输入多输出(MIMO)系统解耦成单个独立的伪线性对象,并提出一种基于RBF神经网络逆控制与非线性比例积分微分(PID)控制相结合的智能控制策略,保证了系统稳定的同时改善了控制系统性能.仿真和应用结果证实了该控制策略具有快速适应对象和过程变化的能力及较强的鲁棒性.     

Development of characteristic equations and robust stability analysis for MIMO move suppressed and shifted DMC

Discrete-time controller and closed-loop transfer functions were developed for move suppressed λ and the recently formulated m-shifted multiple-input-multiple-output (MIMO) dynamic matrix control (DMC). Using these transfer functions, robust analyses were conducted for MIMO plants by varying corresponding delay and gain ratios of the system. In all instances, robust plots indicate that the shifted DMC is less sensitive and hence more robust to variations in the plant parameters than move suppressed DMC. It was shown that the design of these MIMO DMC controllers depends on the plant closed-loop performance and overall stability, since the selection of λ and m directly influences the plant robustness and closed-loop dynamics.     

Predictive feedback control

In this work a new method for designing predictive controllers for linear single-input/single-output systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter which weights the last w predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors. This last feature improves the closed-loop performance to disturbance rejection as shown through simulations of two linear systems and a nonlinear continuous stirred tank reactor.     

MIMO extended predictive control-implementation and robust stability analysis

The objective of this work is to develop a new tuning strategy for multivariable extended predictive control (EPC). A natural concern is the problem of ill conditionality in controlling multi-input multi-output (MIMO) systems. The main advantage of EPC is that it has a simple and effective tuning strategy that results in a well-conditioned system which can achieve tight closed-loop response. Moreover, unlike most existing model predictive control tuning strategies, the proposed strategy establishes a direct relationship between one main tuning parameter for each subprocess of the MIMO system. This tuning method has been derived based on the assumption of an infinite control horizon resulting in powerful stability for the nominal case and in the presence of model uncertainty. This tuning method is applicable to unconstrained multivariable processes, and was proven to have good control on nonsquare systems. The main features of the new tuning strategy are practically illustrated on a MIMO temperature system with improved control performance as compared to move suppressed predictive control.     

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.     

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations.     

Dual mode adaptive fractional order PI controller with feedforward controller based on variable parameter model for quadruple tank process

The Quadruple Tank Process (QTP) is a well-known benchmark of a nonlinear coupled complex MIMO process having both minimum and nonminimum phase characteristics. This paper presents a novel self tuning type Dual Mode Adaptive Fractional Order PI controller along with an Adaptive Feedforward controller for the QTP. The controllers are designed based on a novel Variable Parameter Transfer Function model. The effectiveness of the proposed model and controllers is tested through numerical simulation and experimentation. Results reveal that the proposed controllers work successfully to track the reference signals in all ranges of output. A brief comparison with some of the earlier reported similar works is presented to show that the proposed control scheme has some advantages and better performances than several other similar works.     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.     

叠堆式超磁致伸缩致动器的模型预测滑模控制

根据新型电液伺服阀的驱动要求,设计了叠堆式超磁致伸缩致动器(SGMA),为补偿其固有的非线性,提高位移输出精度,研究了SGMA的控制策略,并对控制策略进行了仿真和实验验证。首先,采用永磁体和GMM棒交替排布的结构形式设计了SGMA,有助于提高偏置磁场的均匀性;然后,根据SGMA的结构特点,将其视为多自由度振动系统,建立了系统的位移输出模型;接着,在输出模型的基础上,结合模型预测控制与滑模控制策略,设计了模型预测滑模控制器;最后,进行了控制策略仿真和实验验证。实验结果表明,模型预测滑模控制器能够实现SGMA的精密控制。在阶跃控制实验中,系统稳定时间低于1.5ms,无超调和稳态误差;在正弦控制实验中,系统最大控制误差约为0.83μm,相对值约为6.9%,证明了控制策略的有效性。