Predictive Iterative Learning Control Research Articles

Event-triggered based predictive iterative learning control with random packet loss compensation for nonlinear networked systems

In this paper, a novel event-triggered predictive iterative learning control (ET-PILC) method with random packet loss compensation (RPLC) mechanism is proposed for unknown nonlinear networked systems with random packet loss (RPL). First, a new RPLC mechanism is designed by utilizing both the historical and predictive data information to avoid the deterioration of control performance due to RPL. Then, a new event-triggered condition is designed based on the proposed RPLC mechanism to save communication resources and reduce computational burden. Moreover, the convergence of the modeling error and tracking control error are analyzed theoretically, and simulation results are given to demonstrate the effectiveness of the proposed method further.

Model Predictive Iterative Learning Control Design for Battery Optimal Electro-Thermal Management Under Daily-Variant State-of-Charge Patterns

Model Predictive Iterative Learning Control Design for Battery Optimal Electro-Thermal Management Under Daily-Variant State-of-Charge Patterns

A two-dimensional model predictive iterative learning control based on the set point learning strategy for batch processes

Although conventional two-dimensional model predictive iterative learning control (2D-MPILC) based on an extended non-minimum state space (ENMSS) model can avoid designing an observer, it only relies on feedback to passively deal with time delay. This passive treatment for time delay hinders the further improvement of control performance. To address this shortcoming, a two-dimensional model predictive iterative learning control strategy based on the set point learning (2D-SPL-MPILC) is proposed. Firstly, a set point learning strategy is developed to improve the ability to deal with time delay. Based on the error of the previous batch, the set point learning strategy perceives the system dynamics in advance, and then outputs this advance perception in the form of the predictive set point. Secondly, a novel ENMSS model is constructed on the basis of the predictive tracking error between the predictive set point and the actual output. Since the predictive tracking error integrates the predictive set point, this novel ENMSS model has certain predictive ability. Finally, based on this novel ENMSS model, a novel two-dimensional model predictive iterative learning control (2D-MPILC) method, which is called 2D-SPL-MPILC method, is designed. Because this controller contains the perception of future process dynamics, it can reduce the impact of time delay and improve the control performance. The case studies on the packing pressure control in injection molding process and batch reactors demonstrate the effectiveness of the presented 2D-SPL-MPILC strategy.

Robust model-based predictive iterative learning control for systems with non-repetitive disturbances

Iterative Learning Control (ILC) is commonly used for batch processes. However, it may face difficulties when dealing with non-repetitive disturbances and inconsistent initial states. In situations with non-repetitive disturbances, the output may disobey constraints and negatively impact tracking performance when using existing predictive ILC algorithms. This paper introduces a new model-predictive ILC incorporating feed-forward and feedback mechanisms. This new approach evaluates and attenuates the impact of non-repetitive disturbances on the output. As a result, constraints are guaranteed, and tracking performance is preserved and improved, even in the presence of non-repetitive disturbances. Furthermore, if the desired trajectory is unattainable, the proposed ILC can robustly track an optimal trajectory while still guaranteeing constraints. The convergence is proven rigorously. Finally, two examples are provided to demonstrate the effectiveness of this new approach.

A novel two‐dimensional PID controller design using two‐dimensional model predictive iterative learning control optimization for batch processes

AbstractIt is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one‐dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two‐dimensional PID controller optimized by two‐dimensional model predictive iterative learning control (2D‐PID‐MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two‐dimensional PID (2D‐PID) controller is developed by combining the advantages of a PID‐type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D‐PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one‐dimension to two‐dimensions. Second, the tuning guidelines of the presented 2D‐PID controller are obtained from the two‐dimensional model predictive control iterative control (2D‐MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D‐MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.

Robust Model Predictive Iterative Learning Control for Iteration-Varying-Reference Batch Processes

Model predictive iterative learning control (MPILC) is a popular approach to control batch systems with repetitive nature, as it is capable of tracking the plant reference trajectory with high accuracy and guaranteed closed-loop stability. However, varying reference trajectory often happens in batch processes, so that the tracking performance of a general MPILC can be deteriorated. With the reference variation being treated as bounded disturbance, this paper incorporates H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control with MPILC to constitute a robust MPILC (RMPILC), so as to restrain the fluctuation of tracking error caused by the varying reference. This RMPILC adopts the linear parameter varying (LPV) model to represent the dynamic property of the nonlinear system. It then solves the input vector of controlled system by optimizing the tracking performance objective function with constraints in the form of linear matrix inequalities. The robust stability and convergence condition of RMPILC are analyzed. The effectiveness of proposed algorithm is verified thorough the simulations on a numerical example, and also a continuous stirred tank reactor (CSTR) system.

Robust predictive iterative learning control for linear time‐varying systems

AbstractA novel control technique is proposed by combining iterative learning control (ILC) with model predictive control (MPC) for a trajectory tracking problem of linear time‐varying systems (LTVSs) in this paper. By introducing prediction mechanism along both of the iteration and time directions instead of only one direction, the control law is successively updated along two directions by learning from the predictive and compensatory information. The new control law can improve convergence speed and effectively suppress the uncertainties in initial states, external disturbances, and model mismatches. The convergence conditions are rigorously derived and analyzed as well. An example is given to illustrate the effectiveness of the obtained results.

Two-Dimensional Iterative Learning Model Predictive Control for Batch Processes: A New State Space Model Compensation Approach

To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design; second, a new error compensation strategy is introduced in the controller design to improve the ensemble control performance. The two merits are combined together to form a new control strategy where the model predictive control and iterative learning control are united based on the 2-D framework. By employing the novel model formulation and the system error compensation approaches, the effects caused by uncertainty in the batch processes can be eliminated gradually from cycle-to-cycle such that the desired control performance will be obtained finally. The effectiveness of the proposed 2D-MPILC is tested on the packing pressure in the injection molding batch process.

Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Aiming at the needs of ultrasonic motor motion control, a new two-dimensional (2D) predictive control objective function is proposed. Different from the existing methods, the objective function consists of three terms, including the product of the control quantity and the error of the previous control process. Based on the objective function, the predictive iterative learning control (ILC) law is derived by using the design method of generalized predictive control (GPC) without specifying ILC law form in advance. An on-line identification method for model parameters is given to realize effective identification under a small amount of data circumstances, and therefore, the parameters of controller are adjusted adaptively according to the identification results. The proposed control method is validated both in simulation and experiment. The experimental results show that the proposed predictive iterative learning control strategy can obtain better control effect than GPC, and has more obvious characteristics of iterative learning control. It can maintain the expected performance under the condition of intermittent loading and replacing the motor. It presents strong robustness.

Novel Generalized Predictive Iterative Learning Speed Controller for Ultrasonic Motors

Aiming at the needs of ultrasonic motor's motion control system, generalized predictive iterative learning control (GPILC) strategy is constructed by integrating iterative learning control and generalized predictive control. By designing 2D objective function with the information of the previous control process, it attempts to integrate the generalized predictive control methods such as multi-step predictive and rolling optimization into the iterative learning control law to improve the iterative learning control effect. An effective design method of the iterative learning control law is proposed. Based on 2D prediction model, the differential optimization of the objective function is carried out to derive the GPILC law. Then, based on the nonlinear Hammerstein model of ultrasonic motor, an inverse compensation method for the motor's nonlinearity is designed to realize the effective compensation for motor's nonlinearity. On this basis, a generalized predictive iterative learning speed controller for ultrasonic motors is designed. The results of simulation and experiment indicate that the proposed control strategy and its design method are effective. The iterative learning process of ultrasonic motor's speed response is gradually convergent, and the control performance is good.

Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking

A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.

A robust integrated model predictive iterative learning control strategy for batch processes

A robust integrated model predictive iterative learning control strategy for batch processes

A just-in-time-learning based two-dimensional control strategy for nonlinear batch processes

In this paper, we study the problem of two-dimensional (2D) integrated model predictive iterative learning control for nonlinear batch processes. A nonlinear process is presented by using local models together with a just-in-time-learning (JITL) method. In order to deal with the problem of massive computation for identifying JITL models, a three-layer searching strategy and an updating mechanism for databases are proposed. First, a dynamic model whose parameters vary with time and batch is established for batch processes. Then, a JITL-based 2D control strategy is devised to realize comprehensive control by combining iterative learning control in batch-axis with model predictive control in time-axis. As a result, not only the closed-loop control performance can be improved, but also the optimization procedure can be simplified. Finally, performance analysis verifies the effectiveness of the proposed methods.

A two-dimensional design of model predictive control for batch processes with two-dimensional (2D) dynamics using extended non-minimal state space structure

A modified two-dimensional (2D) structure based control strategy that incorporates model predictive control (MPC) and iterative learning control (ILC) in 2D sense is developed for batch processes in this article. To enhance the control effect of the conventional 2D model predictive iterative learning control (2D-MPILC), an improved non-minimal form of state space model is utilized in the developed method. Through employing such model, the dynamics of the closed-loop system can be regulated by tuning extra process variables. In other words, additional degrees of freedom are achieved in the novel controller design, and the modified control performance is expected for the proposed algorithm. The simulation results on two processes evaluate the effectiveness of the presented 2D-MPILC strategy finally.

RBFNN-Based Data-Driven Predictive Iterative Learning Control for Nonaffine Nonlinear Systems.

In this paper, a novel data-driven predictive iterative learning control (DDPILC) scheme based on a radial basis function neural network (RBFNN) is proposed for a class of repeatable nonaffine nonlinear discrete-time systems subjected to nonrepetitive external disturbances. First, by utilizing the dynamic linearization technique (DLT) with a newly introduced and unknown system parameter pseudopartial derivative (PPD) and designing a new RBFNN estimation algorithm along the iterative learning axis for addressing the unknown PPD and the unknown nonrepetitive external disturbances, a data-driven prediction model is established. It is theoretically shown that by constructing a composite energy function (CEF) with respect to the modeling error for the first time, the convergence of the modeling error via the proposed DLT-based RBFNN modeling method can be guaranteed, and the convergence speed is tunable. Then, a DDPILC with a disturbance compensation term is designed, and the convergence of the tracking control error is analyzed. Finally, simulations of a train operation system reveal that even if the train suffers from randomly varying load disturbances and nonlinear running resistance, the proposed scheme can make both the modeling error and the tracking control error decrease successively with increasing operation number.

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)-based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV.

Quadratic-Criterion-Based Model Predictive Iterative Learning Control for Batch Processes Using Just-in-Time-Learning Method

In this paper, a new quadratic-criterion-based model predictive iterative learning control (QMPILC) algorithm for tracking problem of batch processes is proposed. In the proposed QMPILC design, a parametric time-varying model consisting of a set of local models is established for nonlinear batch processes by using the just-in-time-learning method. In order to describe the processes more accurately, the model is updated with batch running. On basis of the identification model, iterative learning control is combined with model predictive control based on a quadratic performance criterion, and the control law can be obtained by solving a convex optimization problem. According to the real-time feedback information, the input is updated to reject real-time disturbance. As a result, the proposed QMPILC algorithm improves control performance and optimization efficiency. In addition, the convergence and tracking performance of QMPILC are analyzed. The proposed methods are illustrated on batch reactor. The results are provided to show excellent performance of tracking product qualities.

2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay

In this paper, a 2D terminal constrained model predictive iterative learning control method of batch processes with time delay is proposed to deal with time delay, input and output constraints, and disturbances in batch processes. Firstly, an iterative learning control law is designed for the given batch process; then the state error and output tracking error are introduced, and the original state-space model is converted to an equivalent 2D-FM model. In the meantime, an optimal performance index with terminal constraints is introduced, and an update law is designed to minimize the objective function under input and output constraints. The robust constraint set is adopted that the system state is converged to this set round the desired point. Then, based on the designed optimal performance index and Lyapunov stability theory, the MPC problem is transformed into a linear matrix inequality problem and a sufficient condition is given to ensure the robust asymptotically stability of the closed-loop system. Finally, the validity of the proposed method is proved by the simulation on a stirred tank.

Iterative-learning integral-plus-proportional control of a flexible swing arm system for position trajectory tracking

In the LED packaging industry, the position trajectory tracking of a flexible swing arm system is influenced by the unknown disturbances and parametric uncertainties. In this paper, an iterative-learning integral-plus-proportional (IP) controller is proposed to enhance the efficiency and accuracy of the flexible swing arm system. First, online prediction mechanism is incorporated with the iterative-learning automatic tuning to obtain an optimum of the controller parameters, referred to as predictive iterative-learning control. Then, the adaptive iterative-learning IP controller is derived using the proposed predictive iterative-learning control method. To achieve faster convergence, virtual reference feedback tuning is used to obtain the initial parameters of IP controller. In this method, only input/output measured data of the controlled plant are fully utilized by means of dynamic linearization technique. Moreover, the proposed tuning method can optimize the controller online using experimental data during normal system operation. The convergence and stability properties of the closed-loop system are analyzed. Finally, simulations and experiments on real-time flexible swing arm system show the effectiveness of the predictive iterative-learning IP control method.

2D multi-model general predictive iterative learning control for semi-batch reactor with multiple reactions

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional (2D) general predictive iterative learning control (2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control (ILC) algorithm for a 2D system and designed in the generalized predictive control (GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.