Structure-free model-based predictive signal control: A sensitivity analysis on a corridor with spillback

To achieve high robustness and performance, this article proposes an enhanced observer based on a predictive error model for the deadbeat predictive current control method (DPCC-PEMO). First, the mathematical model of permanent magnet synchronous motor (PMSM), deadbeat predictive current control (DPCC) method, and disturbance analysis are presented. Second, to estimate the trend of current variation in advance under nonlinear disturbance effect of model mismatch, the predictive error model is designed based on the recursive least squares (RLS) algorithm. Furthermore, the enhanced observer combined with the predictive error model (PEMO) is proposed, and its stability and high convergence are proved. Finally, utilizing the PEMO, the predictive current model is established and the proposed DPCC method is developed. The experimental results validate the strong robustness and excellent dynamic tracking performance of the proposed method in PMSM systems.

Model-based predictive control that highly depends on system parameters has been widely investigated. In contrast, model -free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. In this paper, a model free predictive control method based on discrete space vector modulation is used, which increases the effective vector control set by constructing a virtual vector which is composed of linear combination of real vectors. The THD of grid connected current is reduced. Simulation and experimental results confirmed the effectiveness of the proposed method.

This study presents two improved designs of eccentric-shaped permanent magnets and teeth-shaped stators in radial-flux dual three-phase permanent magnet electric machines to reduce cogging torque and torque ripple. The finite element analysis (ANSYS Electromagnetics) has been adopted in simulation, and real radial-flux dual three-phase permanent magnet electric machines have been fabricated in experiment to verify the study. Using the radial-flux dual three-phase permanent magnet electric machines in electric machine systems can improve the reliability and obtain higher output torque. In electrical drives and control, a simplified model-free predictive current control method has been proposed and implemented to drive the radial-flux dual three-phase permanent magnet electric machines, and the control law has been achieved by a TMS320F28377S microcontroller of Texas Instruments. The simplified model-free predictive current control method is senseless to parameter variations and back electromotive force of the permanent magnet electric machines, and only needs current sensors to detect six-phase current. The optimal one has been chosen by 14 various switching modes, which has the minimum cost function, and then the converter can be directly driven and controlled in the next sampling period. The features of the simplified model-free predictive current control method can reduce the algorithm calculation and avoid the defect of conventional model-based predictive current control scheme. A proportional-integral speed controller has also been designed to achieve the speed response of the fixed-speed tracking effect. Finally, the feasibility and effectiveness of the proposed simplified model-free predictive current control method for the dual three-phase permanent magnet electric machines can be verified in the experimental and quantitative results.

Aiming at the robust attitude control issue on reentry vehicle subject to uncertainties and disturbances. A novel optimal predictive attitude control method integrating with a high order sliding mode observer is proposed in this paper. On the one hand, the proposed optimal one-step predictive control approach can guarantee good dynamic performance of the control system, on the other hand, a high order sliding mode observer is applied to estimating the uncertainties and disturbances exactly, which can strengthen the robustness of the control system. System stability is strictly proved by Lyapunov theory and the domains of convergence are given exactly with/without the observer. Finally, an attitude predictive control law is presented for a reentry vehicle with dynamic characteristic of the actuators. Numerical simulations demonstrate the effectiveness of the proposed control law.

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

This study proposed a vehicle state prediction model, which can predict vehicle yaw rate and lateral acceleration in the prediction horizon on the basis of the information of state and control input at current sampling time. This prediction model of lateral acceleration obtained by bringing change rate of sideslip angle and yaw rate into lateral jerk expression, and the prediction result is modified by the steering speed at the same time to elevate the accurateness of this prediction model. The simulation results of constant frequency sine maneuver and double lane change maneuver show that the prediction model could predict the vehicle state in prediction horizon well and prediction error is kept within a small range.

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

The control of the field outlet temperature of a parabolic trough solar field (PTSF) is crucial for the safe and efficient operation of the solar power system but with the difficulties arising from the multiple disturbances and constraints imposed on the variables. To this end, this paper proposes a constraint optimal model-based disturbance predictive and rejection control method with a disturbance prediction part. In this method, the steady-state target sequence is dynamically corrected in the presence of constraints, the lumped disturbance, and its future dynamics predicted by the least-squares support vector machine. In addition, a maximum controlled allowable set is constructed in real time to transform an infinite number of constraint inequalities into finite ones with the integration of the corrected steady-state target sequence. On this basis, an equivalent quadratic programming constrained optimization problem is constructed and solved by the dual-mode control law. The simulation results demonstrate the setpoint tracking and disturbance rejection performance of our design under the premise of constraint satisfaction.

In order to alleviate the congestion between the side road and the freeway, a novel balancing control scheme is proposed based on model free adaptive predictive balancing control (MFAPBC). The proposed MFAPBC is a typical data driven control method, which merely utilize the measured input and output (I/O) data of side road and freeway to update the control input signal. Model free adaptive predictive control (MFAPC) has the advantages of model predictive control (MPC) and model free adaptive control (MFAC); that is, only the I/O data of the controlled plant are used to predict the system output sequence within the prediction horizon and to calculate the control input sequence within the control input horizon, without the controlled plant model involved. The effectiveness of the proposed MFAPBC method is test in simulation for side road and freeway system.

In medium-voltage (MV) drives, the switching frequency is limited to a few hundred Hz, for which high-performance control and modulation schemes are necessary to maintain acceptable current and torque distortion. Forced machine current control (FMCC) is a predictive control strategy for MV drives which was proposed in the early 1980s, which can be formulated for either torque or current control. Recently, model predictive direct torque control (MPDTC) and model predictive direct current control (MPDCC) have been developed, sharing with FMCC the use of hysteresis bounds, switching and prediction horizons. However, the relative performances of these schemes are yet to be compared. Through simulation, this paper compares the schemes across a range of operating points. It is shown that the steady-state performance of MPDxC and FMCC is similar when the switching horizon of MPDxC is limited. However, when the switching horizon is extended, the performance of MPDxC is shown to be superior to FMCC, the horizon of which is inherently restricted.

The long-range predictive control strategy is extended to include a terminal matching condition so that the final control law minimizes the squares of prediction errors over a small future prediction horizon and at steady state. The weighting on the terminal condition is a better alternative than a large prediction horizon, which requires a heavy computational load. It also leads to the formulation of an approximate long range predictive controller with knowledge of only a few initial step response coefficients and the steady state gain. Evaluations of steady state error weighting show that the weighting provides similar effects to ordinary control weighting but in addition has the stabilizing effects of a large predictive control horizon. An adaptive version of this control strategy has been successfully applied to the control of mean arterial blood pressure. © 1997 by John Wiley & Sons, Ltd.

High-end biomedical applications, such as Optical Coherence Tomography (OCT) or Confocal Microscopy (CM) require both precision and speed. The latter is essential in OCT by example to achieve in vivo, real time imaging – with video rate imaging capability. An essential element of this effort to achieve such speeds in OCT by example is the optomechatronic system used for lateral scanning. It usually consists of a dual axis double galvanometer-based scanner (GS). However, GSs are used in a larger variety of applications in biomedical imaging – not only in lateral scanning. Due to the importance of the topic, we have approached different aspects of GSs technology, including scanning and control functions, duty cycle optimization, and minimization of artifacts. The paper proposes a Model-based Predictive Control (MPC) structure for driving the GSs in order to achieve either an improved precision or a higher speed. The predictive control solution was tested for different types of input signals. Reasons for choosing the objective function and the predictive horizons are discussed. The GS was characterized by a second order mathematical model (MM), with the values of the parameters identified experimentally. Simulations were carried out using Matlab Simulink. The control results achieved are compared with the Proportional Integrative Derivative controller with Lags (PID-L1). The conclusions support the proposed control solution and its implementation in applications.

A torque ripple minimising and robust predictive torque control of a doubly fed induction generator (DFIG) is proposed. The proposed method is based on a novel cost function and a robust predictive model. The aim of the proposed cost function is torque ripple reduction beside the torque and flux control. This method predicts the torque and the flux those will be resulted by the feasible voltage vectors for the next step. The best option is chosen and applied based on the proposed cost function. Two expressions are added to the conventional cost function for the torque ripple reduction. These terms minimise the effective value of the torque ripple and the variation of the torque while the main control is not affected. Furthermore, a novel closed‐loop prediction model for DFIG is proposed in order to improve the robustness of the method. Simulation results for the conventional finite control set‐model predictive control and the proposed methods are presented and compared in order to confirm the effectiveness of this method.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

The class of model-based predictive controllers (MBPCs) has been quite successful at overcoming many of the well-known difficulties inherent in nonminimum phase (NMP) systems, e.g., flexible structures, and among the various MBPCs, Unified Predictive Control (UPC) was selected for the control tasks in this paper. Two methods are used to model the tip force of a flexible manipulator. Modal expansion (summation) is a pole but not zero preserving approach, while Infinite Product Expansion preserves both poles and zeros of a system. It is shown that in a flexible structure framework the difference between the exact and modal expansion zeros is significant. The effect of transmission zero mismatch will be observed in a closed loop system, especially when a model based controller is used. Simulation results show that when a UPC is designed based on the modal expansion model, it destabilizes the closed loop system when an exact model is used as the plant. This suggests a severe problem in implementing the designed UPC experimentally. The use of an exact model in a model-based controller design procedure, such as UPC, is therefore suggested. (Author)