Yuanlong Hou

Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

Neural Network Based Active Disturbance Rejection Control of a Novel Electrohydraulic Servo System for Simultaneously Balancing and Positioning by Isoactuation Configuration

To satisfy the lightweight requirements of large pipe weapons, a novel electrohydraulic servo (EHS) system where the hydraulic cylinder possesses three cavities is developed and investigated in the present study. In the EHS system, the balancing cavity of the EHS is especially designed for active compensation for the unbalancing force of the system, whereas the two driving cavities are employed for positioning and disturbance rejection of the large pipe. Aiming at simultaneously balancing and positioning of the EHS system, a novel neural network based active disturbance rejection control (NNADRC) strategy is developed. In the NNADRC, the radial basis function (RBF) neural network is employed for online updating of parameters of the extended state observer (ESO). Thereby, the nonlinear behavior and external disturbance of the system can be accurately estimated and compensated in real time. The efficiency and superiority of the system are critically investigated by conducting numerical simulations, showing that much higher steady accuracy as well as system robustness is achieved when comparing with conventional ADRC control system. It indicates that the NNADRC is a very promising technique for achieving fast, stable, smooth, and accurate control of the novel EHS system.

Control of the Electric Load Simulator Using Fuzzy Multiresolution Wavelet Neural Network with Dynamic Compensation

A fuzzy multiresolution wavelet neural network (FMWNN) controller with dynamic compensation (DC) is proposed to address the complexities of the electric load simulator (ELS). The FMWNN acts as a main torque tracking controller, which takes full advantage of the merits of an ideal sliding mode, fuzzy rules, and multiresolution WNN. The fuzzy algorithm is used to dynamically adjust the weights of the WNN and effectively accelerate the convergence rate. In addition, the DC controller is designed to greatly decrease the effect of the approximation error and guarantee the system stability in the sense of the Lyapunov theory. Finally, the proposed algorithms are carried out on the semiphysical simulation platform, the precision and superiority of which are comparatively verified based on the simulation results.

Development of a Novel Disturbance Observer Based Fractional Order PD Controller for a Gun Control System

Motion control of gun barrels is an ongoing topic for the development of gun control equipment (GCE) with excellent performances. In this paper, a novel disturbance observer (DOB) based fractional order PD (FOPD) control strategy is proposed for the GCE. By adopting the DOB, the control system behaves as if it were the nominal closed-loop system in the absence of disturbances and uncertainties. The optimal control parameters of the FOPD are determined from the loop-shaping perspective, and the Q-filter of the DOB is deliberately designed with consideration of system robustness. The linear frame of the proposed control system will enable the analysis process more convenient. The disturbance rejection properties and the tracking performances of the control system are investigated by both numerical and experimental tests, the results demonstrate that the proposed DOB based FOPD control system is of more robustness, and it is much more suitable for the gun control system with strong nonlinearity and disturbance.

Passivity-Based Control for Rocket Launcher Position Servo System Based on ADRC Optimized by IPSO-BP Algorithm

In order to achieve better motion accuracy and higher robustness of the shipborne rocket launcher position servo system driven by a permanent magnet synchronous motor (PMSM), a passivity-based controller based on active disturbance rejection control (ADRC) optimized by improved particle swarm optimization-back propagation (IPSO-BP) algorithm is proposed in this paper. The convenient method of interconnection and damping assignment and passivity-based control (IDA-PBC) is adopted to establish the port controlled Hamiltonian system with dissipation (PCHD) model of PMSM. To further enhance the robustness and adaptability of traditional ADRC, an BP algorithm is introduced to on-line update the proportional, integral, and derivative gains of ADRC. Furthermore, to improve the learning capability, the improved PSO algorithm is adopted to optimize the learning rates of the back propagation neural networks. The results of numerical simulation and prototype test indicate that the proposed IPSO-BP-ADRC-PBC controller has better static and dynamic performance than the ADRC-PBC and BP-ADRC-PBC controller with fixed learning rate.

Self-Organizing Adaptive Wavelet Backstepping Control Research for AC Servo System

A novel self-organizing adaptive wavelet cerebellar model articulation controller backstepping (SOWCB) control is proposed, aiming at some nonlinear and uncertain factors that caused difficulties in controlling the AC servo system. This controller consists of self-organizing wavelet cerebellar model articulation controller (CMAC) and robust compensator. It absorbs fast learning and precise approaching advantage of self-organizing wavelet CMAC to mimic a backstepping controller, and then robust compensator is added to inhibit influence of the uncertainties on system performance effectively and realize high accuracy position tracking for AC servo system. Moreover, the stability of the control system can be guaranteed by using Lyapunov method. The results of the simulation and the prototype test prove that the proposed approach can improve the steady state performance and control accuracy and possess a strong robustness to both parameter perturbation and load disturbance.

Balancing and positioning for a gun control system based on fuzzy fractional order proportional–integral–derivative strategy

To achieve perfect behavior of the unbalanced barrel of a gun control system, a novel control strategy for simultaneous balancing and positioning of the system is proposed, physically being on the basis of a novel hydraulic cylinder with three cavities. The fuzzy fractional order proportional–integral–derivative controller is developed, and the particle swarm optimization algorithm is adopted for optimal selection of the control parameters for the gun control system. The results demonstrate that the fuzzy fractional order proportional–integral–derivative control strategy can finely improve dynamic performance of the control system, and the nonlinear characteristics of system can be effectively suppressed.

Electric load simulator system control based on adaptive particle swarm optimization wavelet neural network with double sliding modes

In this article, an adaptive particle swarm optimization wavelet neural network with double sliding modes controller is proposed to address the complex nonlinearities and uncertainties in the electric load simulator. The adaptive double sliding modes–particle swarm optimization wavelet neural network algorithm with the self-learning structures and parameters is designed as a torque tracking controller, in which a number of hidden nodes are added and pruned by the structure learning algorithm, and the parameters are online adjusted by the adaptive particle swarm optimization at the same time. Moreover, one conventional sliding mode is introduced to track the time-varying reference command, and the other complementary sliding mode is adopted to attenuate the effect of the approximation error. Furthermore, the relative parameters should comply with some estimation laws on the basis of the Lyapunov theory used to guarantee the system stability. Finally, the simulation experiments are carried out on the hardware-in-the-loop platform for the electric load simulator, the performance of the adaptive double sliding modes–particle swarm optimization wavelet neural network with structure learning is verified compared with some similar control methods. In addition, different amplitudes and frequencies of the reference commands are introduced to further evaluate the effectiveness and robustness of the proposed algorithms.

Passivity-based control for rocket launcher position servo system based on improved active disturbance rejection technology

In order to achieve high motion accuracy and better robustness of the rocket launcher position servo system driven by a permanent magnet synchronous motor, a passivity-based controller based on improved active disturbance rejection control is proposed in this article. The convenient method of interconnection and damping assignment and passivity-based control is adopted to establish the port-controlled Hamiltonian system with dissipation model of permanent magnet synchronous motor. To further enhance the robustness and adaptability of the traditional active disturbance rejection controller, an improved active disturbance rejection control strategy–based radical basis function neural network is introduced to on-line update the proportional and derivative gains of improved active disturbance rejection controller. The results of numerical simulation and bench test indicate that the proposed improved active disturbance rejection control passivity–based control algorithm has advantages of smaller overshoot, fast response, small steady-state error, and strong robustness. It proves that the proposed control scheme is effective and suitable.

Fast template matching based on grey prediction for real-time object tracking

Template matching is a basic algorithm for image processing, and real-time is a crucial requirement of object tracking. For real-time tracking, a fast template matching algorithm based on grey prediction is presented, where computation cost can be reduced dramatically by minimizing search range. First, location of the tracked object in the current image is estimated by Grey Model (GM). GM(1,1), which is the basic model of grey prediction, can use some known information to foretell the location. Second, the precise position of the object in the frame is computed by template matching. Herein, Sequential Similarity Detection Algorithm (SSDA) with a self-adaptive threshold is employed to obtain the matching position in the neighborhood of the predicted location. The role of threshold in SSDA is important, as a proper threshold can make template matching fast and accurate. Moreover, a practical weighted strategy is utilized to handle scale and rotation changes of the object, as well as illumination changes. The experimental results show the superior performance of the proposed algorithm over the conventional full-search method, especially in terms of executive time.