Earticle

Objective To investigate the predictive value of the sympathetic skin response (SSR) in diagnosing complex regional pain syndrome (CRPS) by comparing three diagnostic modalities?SSR, three-phasic bone scans (TPBS), and thermography.Methods Thirteen patients with severe limb pain were recruited. Among them, 6 were diagnosed with CRPS according to the proposed revised CRPS clinical diagnostic criteria described by the International Association for the Study of Pain. SSR was measured in either the hands or feet bilaterally and was considered abnormal when the latency was prolonged. A positive TPBS finding was defined as diffuse increased tracer uptake on the delayed image. Thermographic findings were considered positive if a temperature asymmetry greater than 1.00oC was detected between the extremities.Results Five of 6 CRPS patients showed prolonged latency on SSR (83% sensitivity). TPBS was positive in the 5 CRPS patients who underwent TPBS (100% sensitivity). Thermography was positive in 4 of 5 CRPS patients who underwent the procedure (80% sensitivity). The remaining 7 non-CRPS patients differed on examination. SSR latencies within normal limit were noted in 4 of 7 non-CRPS patients (57% specificity). Results were negative in 4 of 5 non-CRPS patients who underwent TPBS (80% specificity), and negative in 3 of 5 non-CRPS patients who underwent thermography (60% specificity).Conclusion SSR may be helpful in detecting CRPS.

This paper presents methods of model predictive control (MPC) for eco-driving minimizing trip-time and energy- consumption of electric vehicles (EVs). Considering both non- convex and convex optimization problem formulations for MPC- based eco-driving, we compare the performances of nonlinear and linear MPC solutions for high-level planning of vehicle speed and charging in a driving simulation of a Munich– Cologne trip (573 km). The linear MPC can be considered as a convex quadratic programming approximation (i.e., convexified quadratic program) of the original nonlinear MPC, but its performance of optimality is shown to be comparable to the nonlinear counterparts whereas its computation speed is one order of magnitude faster.

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

The predictive control system using model-based predictive control is a very effective way to optimize the present inputs considering the states and future errors of the reference trajectory, but it has a drawback in that a control input matrix must be repeatedly calculated with a long calculation time at every sampling for minimizing future errors in a predictive interval. In this study, we applied the neural network simulating the predictive control method for the trajectory tracking control of the mobile robot to reduce complex control method and computation time which are the disadvantage of predictive control. In addition, the neural network showed excellent performance by the generalization even for a different reference trajectory. Therefore, The controller is designed by modeling the model-based predictive control gains for the reference trajectory using a neural networks. Through the computer simulation, the proposed control method showed better performance than the general predictive control method.

This paper puts forward on a fast distributed information fusion predictive control algorithm for the time-varying system with unknown stochastic system bias. It is based on the distributed fusion estimation algorithms and state-space model. The optimal information fusion rule for this algorithm is weighted by matrices, diagonal matrices and scalars. It can avoid the complicated Diophantine equation, thus obviously reduces the amount of calculation. Via the distributed information fusion algorithm, the comparison of algorithm in this paper with the local sensor, this algorithm improves stability and accuracy for the time-varying system with unknown stochastic system bias. By testing through the three-sensor target tracking control system simulation, this algorithm shows its effectiveness and correctness, and the results of simulation also show no significant difference in error between the three kinds of distributed fusion algorithm. With reduction of calculation using the scalar weighting fusion predictor, the information fusion estimation algorithm presented in this paper also improves the calculation speed and accuracy.

The multi-sensor information fusion predictive control algorithm for discrete-time linear time-invariant stochastic control system is presented in this paper. This algorithm combines the fusion steady-state Kalman filter with the predictive control. It avoids the complex Diophantine equation and it can obviously reduce the computational burden. The algorithm can deal with the multi-sensor discrete-time linear time-invariant stochastic controllable system based on the linear minimum variance optimal information fusion criterion. The fusion method includes the centralized fusion, matrices weighted and the covariance intersection fusion. Under the linear minimum variance optimal information fusion criterion, the calculation formula of optimal weighting coefficients have be given in order to realize matrices weighted. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection fusion algorithm, which can reduce the computational burden. And the relationship between the accuracy and the computation complexities among the three fusion algorithm are analyzed. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.

The multi-sensor information fusion predictive control algorithm for discrete-time linear time-invariant stochastic control system with random time-delay observations is presented in this paper. The algorithm applies the fusion steady-state Kalman filter to the predictive control. It avoids the complex Diophantine equation and it can obviously reduce the computational burden. The algorithm can deal with the multi-sensor discrete-time linear time-invariant stochastic controllable system based on the linear minimum variance optimal information fusion criterion. The fusion method includes the centralized fusion, global optimality weighted measurement fusion. And the two fusion method is completely functionally equivalence. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.

The paper studies the application of a prediction-based scheme for dynamic output feedback control in a networked control system with uncertainties, communication delay and data dropout. In order to make a compensation for communication delay and data dropout, a prediction-based output control scheme is developed. With the given theoretical derivation, the closed-loop networked predictive control system with uncertainties can be formulated into a robust system with a standard form, which makes it convenient to design the controller and analysis closed-loop stability. Simulation examples demonstrate the performance and stability of a networked control system using the proposed predictive output control scheme.

In order to overcome the disadvantage that single-degree-of-freedom PID controller cannot integrate the optimal target tracking performance and anti-interference function, this article puts forward a design of two-degree-of-freedom IMC-PID controller for the time-delay control system according to the Internal Model Control (IMC) principle. It also puts forward a parameter setting method for the two-degree-of-freedom controller based on the maximum sensitivity. First of all, it deduces the relationship between one of the filter parameters with the maximum sensitivity, and determine this parameter according to the maximum sensitivity index, which gives the system strong robustness; then it revises another filter parameter according to the dynamic performance of the system, which gives the system a strong target tracking characteristic. Meanwhile, it conducts the robust stability analysis on the process model mismatch condition, and obtains the conditions for system stability. According to the simulation results, the time-delay control system which is designed based on two-degree-of-freedom IMC-PID controller has good target tracking characteristic, anti-interference performance and robustness, which proves that the parameter setting method which is based on the maximum sensitivity is effective. The negative impact of time delay on the system can be effectively overcome by using the method put forward here.

The paper presents a model predictive control method for continuous-time systems based on subspace identification. It’s developed by reformulating the continuous-time systems using Laguerre filters to obtain the subspace prediction output. Then, the subspace predictors are derived by QR decomposition from input-output and Laguerre matrices. The subspace predictive controller is designed with the subspace predictors. The process control simulations of a fermentation bioreactor system show the effectiveness of the proposed method.

In this paper, an online receding robust predictive control scheme is proposed for input-constrained semi-trailer system with delay and disturbance. The controller method meets the requirements of control constraint and, based on dual-mode control, the method is obtained by online optimization of performance index. The performance index is the cumulative sum of quadratic weighted value of minimal states. Controller output is calculated by means of linear matrix inequality (LMI), and the controller itself can ensure the asymptotic stability and disturbance attenuation of semi-trailer closed-loop system. Finally, simulation results confirm the effectiveness of the method.

The particular interest in Proton Exchange Membrane(PEM) fuel cell is based on the possibility to generate the clean and efficiency power. The safety and high operation issues of system are closely related to the control strategy used for fuel and oxidant supply. In PEM fuel cell the oxygen excess ratio expresses the proportion between oxygen supply and consumption and represents a decisive variable for the safety and normal performance. The main control object in this work is to maintain the oxygen excess ratio at the reference value which is obtained by simulation experiments. This work is focused on the design of the nonlinear generalized predictive control strategy manipulating the air flow rate in order to maintain the oxygen excess ratio. This proposed control strategy is based on GPC, and the sequence of control increments, input increment and output constraints solving is introduced to design proposed stair-like GPC method. The experiment results base on simulation platform of PEM FUEL CELL, is built in MATLAB environment, shows the better control effect comparing with fuzzy-PID switching method.

Response Surface Methodology(RSM) was successfully applied to a process simulator for optimization of Generalized Predictive Control(GPC) tuning parameters. Wireless experimental input/output data obtained from process simulator. GPC algorithm which is written in MATLAB is utilized to wireless temperature control experiments achieved by using MATLAB/Simulink program.The efficiency of the GPC is observed by calculating the integral of the square of the error (ISE) and the integral of the absolute value of the error (IAE) from experimental results which was optimized by the application of RSM. The three independent variables, which had been found the most effective variables on the GPC by screening experiments, were determined as NU, N2 and λ as minimum prediction horizon, maximum prediction horizon and control weighting, respectively. The quadratic models were developed through RSM in terms of related independent variables to describe the ISE and IAE as the two response. Based on statistic analysis, optimum GPC tuning parameters of NU (X1), N2 (X2) and λ (X3) for minimize the ISE were determined to be 1.7922, 1.9453 and 0.0642 and for minimize the IAE were determined to be 1.8880, 1.9752 and 0.0612, respectively. Calculated optimum points of GPC tuning parameters are close to based on ISE and IAE results. The data evaluated from the quadratic model were good agreement with those measured experimentally. The wireless temperature control is successfully applied to the process simulator and wireless control technique is proposed for various application areas.

This paper presents the method of multivariable integrated model predictive control for the nuclear power plant, including designing controller and building the modeling of turbine and once-through steam generator. The simulation results show that the rotate speed of turbine and outlet pressure of steam generator under the multivariable integrated model predictive control is faster steady and smaller overshoot than under the PID control, when the power load of nuclear power plant is changed. Simulation test indicates that the multivariable integrated model predictive control can obtain better control performance for nuclear power plant.

This paper addresses an optimal train trajectory planning and tracking problem for automatic train operation (ATO) with consideration of the train model uncertainty and constraints. Based on the discrete linear multi-points train model, an ATO control algorithm is presented to track piecewise reference by using model predictive control with constraints tightening such that the feasibility and robust convergence of this algorithm are guaranteed under the varying running resistance, automatic train protection (ATP) constraint, and train motor physical limits. Specifically, the features of the algorithm are: (i) taking traction and braking force of locomotives and braking force of carriges into account explicitly; (ii) integrating constraints tightening approach into piecewise reference tracking problem to ensure robustness; (iii) combining the optimal planning level and tracking control level together. Finally two case studies are conducted to verify the effectiveness of the algorithm.

Model predictive control is the family of controllers, makes the explicit use of model to obtain control signal. The reason for its popularity in industry and academia is its capability of operating without expert intervention for long periods. There are various control design methods based on model predictive control concepts. This paper provides review of the most commonly used methods that have been embedded in an industrial model predictive control. The most widely used strategies as Dynamic matrix control (DMC), Model algorithmic control (MAC), Predictive functional control (PFC), Extended prediction self-adaptive control (EPSAC), Extended horizon adaptive control(EHAC) and Generalized predictive control(GPC) have been described with history, basic idea, properties, and their controller formulation.

This paper provides the design steps of a multivariable Adaptive Generalized Predictive Controller AGPC whose duty is to drive a micro-hydro power system that comprises a hydraulic turbine driving a synchronous generator which is connected to an infinite bus via a step-up transformer and a transmission line. The simulation model of the part turbine/generator of micro-hydropower plant was constructed based on mathematical equations that summarize the behavior of the micro-hydro power plant. Multivariable AGPC is considered here because of its wide use in the industry and also at universities, showing good performance and a certain degree of robustness. In this study, the standard multivariable (GPC) algorithm is presented. The model parameters are estimated using an identification algorithm based on Recursive Least Squares (RLS) method. In order to validate the effectiveness of AGPC, simulation studies for the part turbine/generator of micro hydropower plant are used. Encouraging results are obtained that motivate for further investigations.