Xiaoke Fang

Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton

In this paper, a data-driven model-free adaptive sliding mode control (MFASMC) approach is proposed based on a novel transformation and linearization of the robotic exoskeleton dynamics and a discrete time sliding mode with exponential reaching law. The main feature of the approach is that the dynamics of the multi degree-of-freedom (DOF) robotic exoskeleton are transformed and linearized properly for MFASMC, the controller designing depends only on the measured input torque and output velocity of each joint of the exoskeleton and the sliding mode reaching law guarantees the convergence of MFASMC schemes. The proposed control strategy can maneuver the robotic exoskeleton tracking on its desired velocity tightly even when the dynamic parameter of the exoskeleton is time-varying irregularly and uncertainly. Extensive simulation experiments are conducted by a SimMechanics model of the robotic exoskeleton to illustrate the effectiveness of the proposed approaches.

Single-joint repetitive control of upper-limb rehabilitation robot

Based on motion characteristics of the upper-limb rehabilitation robot in the rehabilitation training, the article proposes to use a new method of repetitive control to restraining joint position error for upper-limb rehabilitation robot control system. First, repetitive motion of joint position regards as a fixed-cycle signal, and then designs a repetitive controller according to motion characteristics. The simulation results show that the effect of system tracking is very well and it has higher stability. It expected results have been achieved.

Development of DeviceNet fieldbus intelligent node

The DeviceNet is a low-cost and high-performance fieldbus. It has been widely applied in the industrial automatic fields in recently years, so the demands of DeviceNet product are increased gradually. To satisfy market needs, the hardware and software designs have been proposed for developing intelligent node of DeviceNet in this paper. The hardware design is mainly composed of controller (SJAl000), microprocessor (80CS1), bus transceiver (82C251), photoelectrical isolation (6N137), switches and display portion, etc. The software design is mainly composed of controller initialization, data receiving, data transmitting, etc. Hardware circuit and software flow charts have been given and some problems that should be paid attention to in realizing are indicated at the same time.

Modeling and simulation of iron ore pellet drying and induration process

We present a mathematical model of iron ore pellet drying and induration in an industrial scale application, and validate it against measured data. The model is based on the laws of mass and heat transfer, and incorporates both physical and chemical processes. The partial differential equations of the process kinetic model involve derivatives in time and bed depth. The model can provide an effective tool for the analysis and design of iron ore pellet induration techniques.

Establishment and optimization of heating furnace billet temperature model

In the metallurgical industry, measuring the temperature distribution directly and accurately in billet heating process is a well-known difficult work. To improve the quality of heating billet, a billet temperature prediction model of heating furnace is necessary. Based on the characteristics of furnace section partition control, this paper firstly established a billet temperature prediction model with three serial neural networks as foundation, then optimized this model with the improved dynamically self-adaptive PSO. The simulation indicated that the establishment of this model is easy, the forecast precision and speed are obviously improved, and the match degree of prediction curve and actual curve is highly increased. All of these proved the effectiveness of this model.

Roll eccentricity compensation control for strip rolling mills based on wavelet packet de-noising theory

This study seeks to improve the strip thickness accuracy by adding filter and compensator in strip rolling mills. Based on wavelet packet de-noising theory, this paper designed a wavelet packet analyzer of roll eccentricity (WPARE) to eliminate noises and useless frequencies in rolling force signal. By combining WPARE with a compensator, the rolling force influenced by the workpiece in a rolling process could be examined, and it can be fed back by the hybrid system in time. Meanwhile, the rolling gap with compensation eccentricity was quantitatively deduced. Results from a simulation illustrate that the new method could restrain the influence of eccentricity effectively and improve the thickness precision.

Modelling and Control of S-MAC Based Wireless Sensor Networks Control System with Network-Induced Delay in Industrial

In view of the problems in the maintenance and deployment of wired sensor networks in industrial environment, this paper propose the application of wireless sensor networks to industrial control network. In consideration of the interference of the wireless sensor network and the obstruction of the barrier, a wireless sensor network model for multi hop wireless sensor is established based on S-MAC protocol, and the time delay is analysised for network model. Then, prove the stability of the controller by using the Lyapunov stability, finally gives the simulation. The simulation results show that the model is in line with the actual situation of the industry, and it laid a solid foundation for the establishment of wireless sensor network model in industrial control environment.

Adaptive velocity field control of upper-limb rehabilitation robot

Upper limb rehabilitation robot is used to assist in completing rehabilitation training for patients with upper limb disorder. It is inevitable that the control system is probably disrupted by the patients in the process of rehabilitation training so that the movement of the rehabilitation robot is not smooth. This problem is not conductive to rehabilitation. In view of the contour tracking method with smooth approximation properties, the velocity field control is proposed in this paper to solve the problem. Contour following tasks can be effectively encoded through the use of desired velocity fields. The parameter adaptive method is proposed in this paper to improve the robustness of the control system and meet the needs of different patients for rehabilitation training. The anti-interference ability and adaptive ability are enhanced by the velocity field control and parameter adaptive method. Analysis and simulation result shows that this control strategy can adapt to different patients and has good robustness and stability.

An improved extreme learning algorithm based on truncated singular value decomposition

With respect to the ill-posed problem when calculating output weights of the ELM (Extreme Learning Machine), an improved ELM algorithm based on TSVD (Truncated Singular Value Decomposition) is proposed in this paper. The degree of ill-condition is severe if the hidden layer output matrix has a large condition number. In such case, the output weights computed by general SVD (Singular Value Decomposition) method will be large and unevenly distributed, which would result in a worsened stability and anti-interference ability. Also, the over-fitting phenomenon presented easily. TSVD is an effective regularization method. It can eliminate the influence caused by small singular values and enhance the generalization ability of the model. As for selecting truncation parameter, it is determined by minimizing the GCV (Generalized Cross-Validation) function with the relationship between TSVD and Tikhnovo Regularization. Simulation results illustrate that TSVD-ELM performs higher prediction accuracy than original ELM on data with noise and increases the models robustness. Finally, the proposed method is used to build a soft-sensor model to predict the quality of iron ore pellet and gets an acceptable error rate.

Application of soft sensor in welding seam tracking prediction based on LSSVM and PSO with compression factor

There are some problems with the strip steel heated in the annealing furnace such as the multiple correlations between temperature in the annealing furnace, steel strip tension, tension roll speed and other variables, noise in field data, which lead to the difficulty to predict the time of welding seam achieved air-knife. The least square support vector machine (LSSVM) inductance model optimized by the particle swarm optimization with compression factor (PSO-CF) algorithm is presented for the difficulty of the time prediction in this paper. The improved algorithm can improve PSO convergence accuracy, and effectively avoid falling into local optimum. It can be both the global fitting ability and local fitting ability of least squares support vector machine. The parameters of LSSVM model are optimized by improved PSO-CF algorithm to escape from the blindness of man-made choice. Using the algorithm in prediction of the arrival time and the position of welding seam, the numerical simulation results illustrate good generalization performance and prediction ability of the proposed method.