Synchronous Control Characteristics Analysis of Shield Propulsion Hydraulic System Based on Tracking Differentiator and Self-Adaptive Nonlinear PID

Aiming at the low precision problem of multi-cylinder cooperative propulsion control in different regions of shield propulsion hydraulic systems under conditions of large load changes, this paper proposes a tracking differentiator and self-adaptive nonlinear PID (TD-NPID) control method to improve the synchronous control characteristics of shield propulsion hydraulic systems. First, the working principles of shield propulsion hydraulic systems were analyzed, and a mathematical model and TD-NPID controller were developed. Then, a simulation model was developed in AMESim-MATLAB environment, and the synchronous dynamic performances of fuzzy PID control, conventional PID control, and TD-NPID control were compared and analyzed. The results demonstrated that the shield propulsion hydraulic system with TD-NPID control had better servo tracking ability and steady-state performance than the systems with fuzzy or conventional PID control, which verified the feasibility of the application of TD-NPID control for the synchronous control of shield propulsion hydraulic systems.

Improved Active Disturbance Rejection Control of Dual-Axis Servo Tracking Turntable with Friction Observer

Friction nonlinear disturbance is one of the main factors affecting the control performance of servo tracking system. In this paper, an improved Active Disturbance Rejection Control (ADRC) scheme of dual-axis servo turntable is researched to achieve accurate tracking. Firstly, the mathematical dynamics model of dual-axis servo tracking turntable system is established. The Elastoplastic model is used to describe nonlinear friction, in which the immeasurable part is extended to be a new state. Secondly, considering the smooth and monotonic increasing property of hyperbolic tangent function, an improved tracking differentiator is introduced, which can provide better noise attenuation performance. Thirdly, based on adjustable parameter systematic pole placement method, the fuzzy control algorithm is applied to realize the intelligent tuning of the improved Extended State Observer (ESO) gains, in which the input of the fuzzy controller is the estimation error, while the output is the observer bandwidth. Finally, the improved ADRC system is transformed into a Lurie system, then the extended circle criteria are adopted to analyze the absolute stability of the proposed system. Simulation and experimental verification of the improved ADRC method for the dual-axis turntable tracking servo system are conducted. Results illustrate the effectiveness and robustness of the proposed controller.

Novel control strategy for non-minimum-phase unstable second order systems: generalised predictor based approach

A control structure based on generalized predictor is proposed to control non-minimum phase unstable second order processes with time delay. The scheme contains a predictor structure and a direct synthesis method based primary controller for servo tracking. The predictor structure consists of two filters acting on input and current output which are designed to provide noise attenuation and disturbance rejection. A set-point filter minimises the overshoot caused by the introduction of additional zeros of the controller in the overall closed loop transfer function so as to smooth the tracking performance. Different second order unstable time delay systems are considered and Integral Absolute Error (IAE) and Total Variation (TV) measures are used for comparing the performances quantitatively. The method is implemented experimentally on an inverted pendulum. The proposed predictive strategy is found to provide enhanced control performances in comparison to the existing literature methods.

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

Magnetic Levitation System (MLS) objective is to levitate objects to the desired height without any contact. MLS is highly nonlinear and inherently unstable. Such a system imposes a challenge when designing robust and high-performance controllers. This paper presents the design of a Sliding Mode (SM) controller with an Integral term called SM-I controller to achieve the desired levitation against nonlinearities and uncertainties of the system. The controller parameters are tuned using the Equilibrium Optimizer (EO) algorithm. The Effectiveness of the proposed controller is validated by simulation results. Simulations are performed for servo tracking with and without perturbations in the MLS parameters. The proposed controller is compared with the conventional SM, LQR, and PID controllers to show its superiority. The results prove that the SM-I is more efficient than the other controllers.

Concurrent-learning-based visual servo tracking and scene identification of mobile robots

PurposeThe purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process.Design/methodology/approachBy using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously.FindingsThe concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully.Originality/valueOn the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.

Two Degree-of-Freedom µ Synthesis Control With Kalman Filter for Flight Environment Simulation Volume With Sensors Uncertainty

Flight Environment Simulation Volume (FESV) is the most important subsystem of Altitude Ground Test Facilities (AGTF). Its control precision of temperature and pressure determines the level of test ability of AGTF. However, in practice, the sensor hysteresis and noise may greatly affect the control precision of FESV. To improve the control performance of FESV in practice, a new control structure of two degree-of-freedom (DOF) μ synthesis control with the extended Kalman filter (EKF) considering actuators and sensors uncertainty is proposed, which constitutes a core support part of the paper. For the problem of sensors de-noising, an EKF is devised to provide a credible feedback signal to the two DOF μ controller. Aiming at the problem of reference command’s rapid change, one freedom feed forward is adopted, while another freedom output feedback is used to ensure good servo tracking as well as disturbance and noise rejection; furthermore to overcome the overshoot problem and acquire dynamic tuning, an integral is introduced in inner loop; additionally, two performance weighting functions are designed to achieve robustness and control energy limit considering the uncertainties in system. In order to verify the effectiveness of the designed two DOF μ synthesis controller with EKF, we suppose a typical engine test condition with Zoom-Climb and Mach Dash and consider time delay and Gaussian noise in the sensors. The simulation results show that the designed two DOF μ synthesis controller with EKF has good servo tracking and noise rejection performance and the relative steady-state and transient errors of temperature and pressure are both less than 0.1% and 0.2% respectively. Additionally, we validate the robust performance of the designed two DOF μ controller with EKF by using the upper bound value of the uncertainty parameters. Furthermore, to verify the advantage of the designed two DOF μ controller with EKF, we compare its control results with those of without EKF and μ controller without considering sensor time delay. The comparison results show that the designed two DOF μ controller with EKF provides better performance. Finally, to verify the advantage of μ synthesis controller, we designed a PID controller and compare the simulation result with μ controller, the comparison result show that the designed μ controller is better than PID controller.