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This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method. • The compound control method based on adaptive model predictive control and modified disturbance observer is proposed. • The proposed control method is proposed to improve the disturbance rejection performance of the uncalibrated and constrained image-based visual servoing system. • Unlike the traditional disturbance observer, the modified disturbance observer here is designed based on the estimated plant model. • The identification algorithm is incorporated into the adaptive model predictive control to minimize the model uncertainty and the influences of the disturbance observer. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper. [Copyright &y& Elsevier]

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. [ABSTRACT FROM AUTHOR]

This paper discusses a nonlinear Model Predictive Control (MPC) algorithm for multiple-input multiple-output dynamic systems represented by cascade Hammerstein–Wiener models. The block-oriented Hammerstein–Wiener model, which consists of a linear dynamic block embedded between two nonlinear steady-state blocks, may be successfully used to describe numerous processes. A direct application of such a model for prediction in MPC results in a nonlinear optimisation problem which must be solved at each sampling instant on-line. To reduce the computational burden, a linear approximation of the predicted system trajectory linearised along the future control scenario is successively found on-line and used for prediction. Thanks to linearisation, the presented algorithm needs only quadratic optimisation, time-consuming and difficult on-line nonlinear optimisation is not necessary. In contrast to some control approaches for cascade models, the presented algorithm does not need inverse of the steady-state blocks of the model. For two benchmark systems, it is demonstrated that the algorithm gives control accuracy very similar to that obtained in the MPC approach with nonlinear optimisation while performance of linear MPC and MPC with simplified linearisation is much worse. [ABSTRACT FROM AUTHOR]

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a novel predictive controller for single-input/single-output (SISO) integrating systems, which can be directly applied without pre-stabilizing the process. The control algorithm is designed on the basis of the tested step response model. To produce a bounded system response along the finite predictive horizon, the effect of the integrating mode must be zeroed while unmeasured disturbances exist. Here, a novel predictive feedback error compensation method is proposed to eliminate the permanent offset between the setpoint and the process output while the integrating system is affected by load disturbance. Also, a rotator factor is introduced in the performance index, which is contributed to the improvement robustness of the closed-loop system. Then on the basis of Jury’s dominant coefficient criterion, a robust stability condition of the resulted closed loop system is given. There are only two parameters which need to be tuned for the controller, and each has a clear physical meaning, which is convenient for implementation of the control algorithm. Lastly, simulations are given to illustrate that the proposed algorithm can provide excellent closed loop performance compared with some reported methods. [Copyright &y& Elsevier]

Abstract: Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse. [Copyright &y& Elsevier]

Abstract: This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series–parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time. [Copyright &y& Elsevier]

Abstract: This paper makes two key contributions. First, it tackles the issue of the availability of constrained predictive control for low-level control loops. Hence, it describes how the constrained control algorithm is embedded in an industrial programmable logic controller (PLC) using the IEC 61131-3 programming standard. Second, there is a definition and implementation of a novel auto-tuned predictive controller; the key novelty is that the modelling is based on relatively crude but pragmatic plant information. Laboratory experiment tests were carried out in two bench-scale laboratory systems to prove the effectiveness of the combined algorithm and hardware solution. For completeness, the results are compared with a commercial proportional–integral–derivative (PID) controller (also embedded in the PLC) using the most up to date auto-tuning rules. [Copyright &y& Elsevier]

Abstract: The paper addresses the problem of designing a robust output/state model predictive control for linear polytopic systems with input constraints. The new predictive and control horizon model is derived as a linear polytopic system. Lyapunov function approach guarantees the quadratic stability and guaranteed cost for closed-loop system. The invariant set and an algorithm approach similar to Soft Variable-Structure Control (SVSC), ensures input constraints for the model predictive plant control system. In the proposed control scheme, the required on-line computation load is significantly less than in MPC literature, which opens the possibility to use these control design schemes not only for plants with slow dynamics, but also for faster ones. [Copyright &y& Elsevier]

Abstract: This paper studies the application of robust model predictive control (MPC) in a constraint process suffering from time-delay uncertainty. The process is described using a transfer function and sampled into a discrete model for computer control design. A polytope is firstly developed to describe the uncertain discrete model due to the process’s time-delay uncertainty. Based on the proposed description, a linear matrix inequality (LMI) based MPC algorithm is employed and modified to design a robust controller for such a constraint process. In case studies, the effect of time-delay uncertainty on the control performance of a standard MPC algorithm is investigated, and the proposed description and the modified control algorithm are validated in the temperature control of a typical air-handling unit. [Copyright &y& Elsevier]

Abstract: This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system. [Copyright &y& Elsevier]

Abstract: This paper proposes a new method for automatic tuning of the Smith predictor controller based on a Repetitive Control (RC) approach. The method requires the input of a periodic reference signal which can be derived from a relay feedback experiment. A modified repetitive control scheme repetitively changes the control signal to achieve tracking error convergence. Once a satisfactory performance is achieved through the learning control, the parameters of the Smith predictor controller can be computed from the signals using a nonlinear least squares algorithm. The same relay feedback experiment can provide an initial parameter vector for an efficient implementation of the parameter estimation. Simulations and experimental results will be furnished to illustrate the effectiveness of the proposed tuning method. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, .an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control. [ABSTRACT FROM AUTHOR]

This paper describes the use of pseudo-partial derivative (PPD) to dynamically linearize a nonlinear system, and aggregation is applied to the predicted PPD, resulting in a model-free adaptive predictive control algorithm for a nonlinear system. The algorithm design is only based on the PPD derived online from the input/output data of the controlled process, however it does provide bounded input/output sequence and setpoint tracking without steady- state error. A detailed discussion on parameter selection is also provided. To show the capability of the algorithm, simulations of a time-delay plant and a pH neutralization process show that the proposed method is effective for system parameter perturbation and external disturbance rejection. [ABSTRACT FROM AUTHOR]

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes. [ABSTRACT FROM AUTHOR]

This paper proposes and discusses a model predictive control approach to hybrid systems with discrete inputs only. The algorithm, which takes into account a model of a hybrid system, described as a mixed logical dynamical system, is based on a performance-driven reachability analysis. The algorithm abstracts the behavior of the hybrid system by building a "tree of evolution." The nodes of the tree represent the reachable states of a process, and the branches connect two nodes if a transition exists between the corresponding states. A cost-function value is associated with each node, and based on this value the exploration of the tree is driven. As soon as the exploration of the tree is finished, the corresponding input is applied to the system and the procedure is repeated. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) offers several advantages for control of chemical processes. However, the standard MPC may do a poor job in suppressing the effects of certain disturbances. This shortcoming is mainly due to the assumption that disturbances remain constant over the prediction horizon. In this paper, a simple disturbance predictor (SDP) is developed to provide predictions of the unmodeled deterministic disturbances for a simplified MPC algorithm. The prediction is developed by curve fitting of the past information. A tuning parameter is employed to handle a variety of disturbance dynamics and a procedure is presented to find an optimum value of the tuning parameter online. A comparison is made with the commonly used disturbance prediction on three example problems. The results show that an improved regulatory performance and zero offset can be achieved under both regular and ramp output disturbances by using the proposed disturbance predictor. [ABSTRACT FROM AUTHOR]

In this paper, a new method of multivariable predictive control is presented. The main advantage of a predictive approach is that multivariable plants with time delays can be easily handled. The proposed control algorithm also introduces a compact and simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. The algorithm of the proposed multivariable predictive control is developed, designed, and implemented on an air-conditioned system. The stability of the proposed control law is discussed. [ABSTRACT FROM AUTHOR]