Controlled Auto-regressive Integrated Moving Average Research Articles

Adaptive generalized predictive control for dynamic positioning system of ships with model unknown parameters

This paper proposes an adaptive generalized predictive control for dynamic positioning of ships with unknown model parameters and unknown time-varying environmental disturbances. The controlled auto-regressive integrated moving average (CARIMA) model is employed to describe the movements of ships with model parameter and disturbance uncertainties. The recursive least square method with forgetting factor is utilized to design adaptive laws to estimate the CARIMA model parameters online. Considering both the positioning accuracy and the change intensity of control increments, a cost function is constructed. The optimal control increments that minimize the cost function are obtained by using Lagrange multiplier method. Then, the adaptive control law is designed to maintain ship’s position and heading at desired values. Finally, simulation studies in different cases are carried out and simulation results demonstrate the effectiveness of the proposed control scheme.

Data‐driven generalized predictive control for car‐like mobile robots using interval type‐2 T‐S fuzzy neural network

AbstractIn this paper, for the nonlinear influencing factors such as uncertainty and external disturbance existing in the actual driving process of the car‐like mobile robot (CLMR), a data‐driven generalized predictive control (GPC) method based on interval type‐2 T‐S fuzzy neural network (IT2TSFNN) is proposed for the trajectory tracking of CLMR. The controlled auto‐regressive integrated moving average (CARIMA) model of the mobile robot is established by analyzing data samples and using IT2TSFNN. Then, a generalized predictive controller is designed for the CARIMA model. Also, the global convergence of IT2TSFNN is verified by the Stone‐Weirstrass theorem. Unlike most previous results, the proposed method does not rely on the mathematical model of the mobile robot but only on the historical data of its operation. Finally, the simulation results show that the proposed method can avoid repeatedly debugging parameters, deal with the influence of uncertain factors, and obtain higher tracking accuracy.

An Adaptive Takagi–Sugeno Fuzzy Model-Based Generalized Predictive Controller for Pumped-Storage Unit

In order to improve the control performance and suppress the “S” characteristics area instability of the pumped-storage unit (PSU), this paper proposes an adaptive Takagi-Sugeno fuzzy model-based generalized predictive controller (ATS-GPC) for the PSU. First, the T-S fuzzy model is used to obtain the controlled autoregressive integrated moving average (CARIMA) model, in which the fuzzy C-means (FCM) clustering algorithm is used for the identification of antecedent parameters and the least square method (LSM) is used to obtain the consequent parameters. Meanwhile, the T-S fuzzy model can be online adjusted according to the real-time tracking error feedback to decrease the influence of the initial offline trained fuzzy model. Then, the generalized predictive controller is designed for the PSU based on the CARIMA. Finally, some numerical simulation experiments including the start-up process, frequency disturbance process, frequency tracking experiments, and robustness analyses have been conducted to verify the proposed method. The experiments results have shown that the proposed ATS-GPC can significantly improve the control performance of the PSU and effectively suppress the unstable operation in “S” characteristics area. In addition, the strong robustness of the proposed controller is verified.

An improved generalized predictive control algorithm based on the difference equation CARIMA model for the SISO system with known strong interference

ABSTRACT The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC.

Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.

一般化最小分散評価に基づく定値制御データからのデータ駆動型PIDゲイン調整

This paper considers a PID gain tuning method based on generalized minimum variance evaluation. The method derives the PID gains for reducing the variance of the generalized output from regulatory control data disturbed by colored noise. Advantages of the method include the use of normal operating data with no additional experiment for PID tuning. Numerical examples for a CARIMA (Controlled Auto-Regressive Integrated Moving Average) model and a liquid level control model show the proposed method is effective for not only colored noise but also stochastic disturbances combined with periodical rectangular deterministic signals. The simulation results demonstrate that the proposed approach has robustness to model mismatch of disturbance characteristics.

Application of nonlinear model predictive control based on swarm optimization in power systems optimal operation with wind resources

With the increased share of power generation based on wind energy, the complexity of the unit commitment (UC) and economic dispatch (ED) problems increases due to the stochastic nature of wind power. Therefore, an accurate and fast optimization method is needed when the generation process involves large quantities of wind sources to effectively manage generation mix and load requirements. In this paper, model predictive control (MPC) is used to solve power system UC/ED problems with the presence of wind energies. Because the UC/ED problem is nonlinear, non-convex and mixed integer problem, the MPC must be used as nonlinear. To produce a nonlinear MPC (NMPC), MPC must be integrated with a fast optimization methodology. This paper presents a generic mathematical formula for a NMPC, integrated with swarm optimization technique to describe the nonlinear behavior in the mathematical formulation. This new formulation will be called swarm model predictive control (SMPC) optimization. The control model will be able to address the effect of the system disturbances and fluctuations using a controlled autoregressive integrated moving average (CARIMA). A general form of future predictions can be expressed as a function of input and output past data, and a future control sequence, and the degree of freedom in the SMPC problem. Also, the prediction part improves the swarm technique, because it identifies the size of search space in a better way. In this paper, UC schedule is designed using the swarm technique offline, while ED is solved using the proposed SMPC optimization method on real-time basis and fed into the automatic generation control system. There is no load deficit in real time ED results in less spinning reserve requirements compared with swarm optimization and dynamic program techniques used discretized load. The system under study is a standard IEEE 30 bus modelled in MATLAB environment, with all data from the city of Florida, USA.

Generalized Predictive Control Applied to Motor Drive Axis

The topic of this article is the adaptive generalized predictive control (GPC) applied to the control of the speed of a digital axis. The system is used in CNC machine tools. Usually, the control of digital axes must obey quickly and effectively to changes in the input variable and continuously adapting with the machining conditions, and oppose the influence of external disturbances. However, The Online Recursive Least Squares with Forgetting factor was adopted to estimate inreal-time the system parameters and adjust instantaneously GPC controller parameters based on a modeling CARIMA (Controlled Auto-Regressive Integrated Moving Average). The results of this control architecture have achieved the desired local performance with good rejection of load disturbances and a good robustness for the parametric variations due to operating point changes.

Generalized Predictive Control Algorithm with Real-Time Simultaneous Modeling and Tuning

A dual adaptation generalized predictive controller (DA-GPC) capable of synchronous modeling and tuning in real time is proposed. The distinctive feature of the proposed controller is the utilization of a second order discrete time controlled auto-regressive integrated moving average (CARIMA) model to capture all possible variations of numerator dynamics via the variable forgetting factor recursive least squares (VFF-RLS) algorithm. The evolving dynamic model is then used to tune, in real time, the value of the move suppression weight. Simulation studies on two benchmark process control problems, i.e., the Van de Vusse nonlinear isothermal stirred tank reactor (with dead time) and the strong acid–base neutralization process, revealed the improved performance of the DA-GPC scheme over the traditional model adaptive GPC and the conventional GPC schemes in terms of set point tracking. Further simulation studies revealed that the DA-GPC scheme was adept in rejecting load disturbances in the process. The good performance attained was attributed to the judicious adaptation of the move suppression weight. Moreover, a user-friendly aspect of the DA-GPC scheme is that the only parameter requiring manual tuning is the VFF-RLS parameter (σ), which may span a large range without materially affecting the efficacy of this adaptive controller.

Model Predictive Control of Filament Tension in Textile Winding Process

Abstract. Yarn tension controlling at heart is tuning servo-motor speed on a restricted range. In this paper, we proposed an advanced model predictive control strategy to solving filament winding system time-delay problem. The application of controlled auto-regressive integrated moving-average (CARIMA) model was described the dynamic time-varying character. The parameters of CARIMA model were solved by recursive least squares parameter estimation method through input-output data. And using the receding optimization method acquire controlling amount in the future. The result of simulation shows that the method is effective and the control structure is simple.

Online elicitation of mamdani-type fuzzy rules via TSK-based generalized predictive control

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

Parametric and nonparametric model based control of a packed distillation column

Parametric and nonparametric model based control systems were applied to control the overhead temperature of a packed distillation column separating methanol–water mixture. Experimental and theoretical studies have been done to observe the efficiency and performance of both control systems. Generalized predictive control (GPC) system based on a parametric model has been tried to keep the overhead temperature at the desired set point. First, a parametric model which is controlled auto regressive integrated moving average (CARIMA) was developed and then the parameters of this model were identified by applying pseudo random binary sequence (PRBS) and using Bierman algorithm. After that this model was used to design the GPC system. Tuning parameters of the GPC system have been calculated using the simulation program of the packed distillation column. Using the predicted parameters, experimental and theoretical GPC systems were found very effective in controlling the overhead temperature. Dynamic matrix control (DMC) system based on a nonparametric model has been used to track the overhead temperature of the packed distillation column. For this purpose, a nonparametric model known as the dynamic matrix was determined using the reaction curve method. A step change in heat input to the reboiler was applied to the manipulated variable and the temperature of the overhead product was observed. After that, the dynamic matrix was used to design the DMC system. Several calculations have been done to define the DMC control parameters. The best values of the tuning parameter were used to realize the DMC system for controlling the overhead temperature experimentally and theoretically. In the presence of some disturbances, the DMC system gives oscillation and offset in experimental studies.

Adaptive Fuzzy TSK Model-Based Predictive Control Using a Carima Model Structure

A new long-range predictive control algorithm for non-linear processes is proposed and is based on the popular Generalized Predictive Control (GPC) algorithm using the Takagi-Sugeno Kang (TSK) piece-wise fuzzy modelling approach. The proposed fuzzy modelling approach is integrated with the proposed control algorithm to form an adaptive control scheme based on a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model structure. The performance of the adaptive control scheme is assessed using a series of experiments on the binary distillation column and on the Continuous Stirred Tank Reactor (CSTR) system.

Generalized predictive control of a robotic manipulator with hydraulic actuators

SUMMARYThis paper presents some aspects of the behavior of hydraulically actuated heavy duty manipulators. This category of manipulators is used extensively in large resource based industries and any improvement in efficiency may result in major financial benefits. In this paper an adaptive control algorithm is used for a two rigid link manipulator driven by hydraulic actuators. The dynamic model of the manipulator is derived as well as the models of the hydraulic actuators including compliance, dead time and full dynamics of the servo valves. An adaptive control algorithm is considered since changes occur on-line in the system's parameters. The adaptive algorithm used is Generalized Predictive Control (GPC). The GPC uses a controlled autoregressive integrated moving average (CARIMA) type model and a cost function that minimizes a predicted future output error and future weighted control inputs to the plant, resulting in a sequence of future control increments. The procedure, in this work, does not separate the hydraulic actuator and the link dynamics into separate sub-systems, but controls them as one system. The changes in the system's parameters due to the hydraulics or the link dynamics can be estimated and the coefficients of the model adjusted without the necessity of identifying the exact cause of the changes.It was found in this work that the variations of the GPC control horizon can lead to faster response during transients and significantly reduced overshoot in the nonlinear hydraulic actuation system. An on-line change of the maximum output horizon is also introduced.This work shows the analysis and results of a two link manipulator with hydraulic actuators. It can be implemented on any hydraulically actuated manipulator with any number of links and actuators.Numerical simulations are performed on a Vax 3200 computer and the results are presented.

Adaptive generalized predictive controller for MIMO systems

The multivariate generalized predictive controller (GPC) is studied (Clarke 1987a, Shah 1987). The multivariable GPC algorithm is presented in a stochastic framework, and a model-following capability is introduced in the control law. The method is described for a controlled autoregressive integrated moving average (CARIMA) model, and for the equivalent state-space representation in its innovation form. The closed-loop system equations are derived and analyzed, using the state-space approach.