Constrained Tracking Control by Gain-Scheduled Feedback With Optimal State Resets: A General Servo Problem and an Online Optimization Method

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Nobutaka Wada ◽  
Hidekazu Miyahara ◽  
Masami Saeki
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Control Method ◽  
Actuator Saturation ◽  
Tracking Error ◽  
Reference Signal ◽  
Optimal Solution ◽  
Optimization Method ◽  
Experimental Result

In this paper, a tracking control problem for discrete-time linear systems with actuator saturation is addressed. The reference signal is assumed to be generated by an external dynamics. First, a design condition of a controller parameterized by a single scheduling parameter is introduced. The controller includes a servo compensator to achieve zero steady-state error. Then, a control algorithm that guarantees closed-loop stability and makes the tracking error converge to zero is given. In the control algorithm, the controller state as well as the scheduling parameter is updated online so that the tracking control performance is improved. Then, it is shown that the decision problem of the scheduling parameter and the controller state can be transformed into a convex optimization problem with respect to a scalar parameter. Based on this fact, we propose a numerically efficient algorithm for solving the optimization problem. Further, we propose a method of modifying the control algorithm so that the asymptotic tracking property is ensured even when the numerical error exists in the optimal solution. A numerical example and an experimental result are provided to illustrate effectiveness of the proposed control method.

Composite Nonlinear Feedback for Vehicle Active Front Steering

2014 ◽  
Vol 663 ◽  
pp. 127-134 ◽  
Author(s):  
M.H. Che Hasan ◽  
Y.M. Sam ◽  
Ke Mao Peng ◽  
Muhamad Khairi Aripin ◽  
Muhamad Fahezal Ismail
Keyword(s):  
Control Method ◽  
Actuator Saturation ◽  
External Disturbance ◽  
Reference Signal ◽  
Stability Control ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Active Front Steering ◽  
Yaw Stability ◽  
Fast Settling

In this paper, Composite Nonlinear Feedback (CNF) is applied on Active Front Steering (AFS) system for vehicle yaw stability control in order to have an excellent transient response performance. The control method, which has linear and nonlinear parts that work concurrently capable to track reference signal very fast with minimum overshoot, fast settling time, and without exceed nature of actuator saturation limit. Beside, modelling of 7 degree of freedom for typical passenger car with magic formula to represent tyre nonlinearity behaviour is also presented to simulate controlled vehicle as close as possible with a real situation. An extensive computer simulation is performed with considering a various profile of cornering manoeuvres with external disturbance to evaluate its performance in different scenarios. The performance of the proposed controller is compared to conventional Proportional Integration and Derivative (PID) for effectiveness analysis.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

scholarly journals Model-Free Optimized Tracking Control Heuristic

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 49 ◽  
Author(s):  
Ning Wang ◽  
Mohammed Abouheaf ◽  
Wail Gueaieb ◽  
Nabil Nahas
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Reference Signal ◽  
Control Algorithms ◽  
Learning Tools ◽  
Control Effort ◽  
Control Loops ◽  
Model Free ◽  
Architectural Framework ◽  
Model Free Control

Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

Synthesis of Controllers for MIMO Systems with Time Response Specifications

2014 ◽  
Vol 3 (3) ◽  
pp. 25-52 ◽  
Author(s):  
Maher Ben Hariz ◽  
Wassila Chagra ◽  
Faouzi Bouani
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Closed Loop ◽  
Controller Design ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Time Response ◽  
Step Response ◽  
Global Optimization Method

This paper proposes the design of fixed low order controllers for Multi Input Multi Output (MIMO) decoupled systems. The simplified decoupling is used as a decoupling system technique due to its advantages compared to other decoupling methods. The main objective of the proposed controllers is to satisfy some desired closed loop step response performances such as the settling time and the overshoot. The controller design is formulated as an optimization problem which is non convex and it takes in account the desired closed loop performances. Therefore, classical methods used to solve the non convex optimization problem can generate a local solution and the resulting control law is not optimal. Thus, the thought is to use a global optimization method in order to obtain an optimal solution which will guarantee the desired time response specifications. In this work the Generalized Geometric Programming (GGP) is exploited as a global optimization method. The key idea of this method consists in transforming an optimization problem, initially, non convex to a convex one by some mathematical transformations. Simulation results and a comparison study between the presented approach and a Proportional Integral (PI) controller are given in order to shed light the efficiency of the proposed controllers.

Design of Optimum Controller of APT Fine Tracking Control System

2013 ◽  
Vol 712-715 ◽  
pp. 2738-2741 ◽  
Author(s):  
Ming Qiu Li ◽  
Shu Hua Jiang
Keyword(s):  
Pid Control ◽  
Tracking Control ◽  
Control Algorithm ◽  
Tracking System ◽  
Dynamic Performance ◽  
Tracking Error ◽  
Stable State ◽  
Response Speed ◽  
Optimum Control ◽  
Performance Requirements

APT (Acquisition, Pointing, and Tracking) system of space laser communication adopts compound axis structure; it consists of coarse tracking and fine tracking system. Its response speed and tracking precision mainly rests with the fine tracking system. Traditional PID control algorithm often is used in APT fine tracking system. In order to improve the dynamic performance of the system and decrease the tracking error, optimum control technology was adopted in this paper. On the basis of considering the system dynamic performance requirements and tracking precision requirement, optimum controller was designed. The simulation result shows that the bandwidth of APT fine tracking system is up to 1310 Hz, and the stable state error is less than 0.002. Compared with PID control, optimum control can improve the tracking performance of system.

Neural Network Based Tracking Control of Mechanical Systems

1999 ◽  
Vol 121 (1) ◽  
pp. 148-154
Author(s):  
T. Efrati ◽  
H. Flashner
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Optimization Problem ◽  
Simple Structure ◽  
Tracking Error ◽  
Mechanical Systems ◽  
Closed Loop System ◽  
Control Effort ◽  
Learning Procedure ◽  
On Line

A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

Control Algorithm Study on Cantilever Machine Teaching-Playback

2014 ◽  
Vol 635-637 ◽  
pp. 1212-1215
Author(s):  
Ruo Han Liu ◽  
Chun Hua Li
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Control Method ◽  
Tracking Error ◽  
Cutting Parameters ◽  
Configuration Software ◽  
Monitoring Site ◽  
Software Monitoring ◽  
Operation Panel

In order to realize intelligent control, the cutting trajectory of TBM research machine adopts the principle of teaching and reappearing cutting trajectory control method, combining with the control system of teaching and reappearing and SIMATIC C7, operation interface is realized by using configuration software monitoring, monitoring site visually through the operation panel parameters change, timely adjust the cutting parameters. The experimental results show that the tracking error within the scope of the permit. The method to improve the intelligence of machine cutting control provides a reference basis.

Nesting Arbitrary Shapes Using Geometric Mating

2002 ◽  
Vol 2 (3) ◽  
pp. 171-178
Author(s):  
Chan Yu ◽  
Souran Manoochehri
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Optimization Method ◽  
The Other ◽  
Reasonable Time ◽  
Common Features ◽  
Object Relative ◽  
Arbitrary Shapes

A genetic algorithm-based optimization method is proposed for solving the problem of nesting arbitrary shapes. Depending on the number of objects and the size of the search space, realizing an optimal solution within a reasonable time may not be possible. In this paper, a mating concept is introduced to reduce the solution time. Mating between two objects is defined as the positioning of one object relative to the other by merging common features that are assigned by the mating condition between them. A constrained move set is derived from a mating condition that allows the transformation of the object in each mating pair to be fully constrained with respect to the other. Properly mated objects can be placed together, thus reducing the overall computation time. Several examples are presented to demonstrate the efficiency of utilizing the mating concept to solve a nesting optimization problem.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

2010 ◽  
Vol 139-141 ◽  
pp. 1945-1949
Author(s):  
Tian Pei Zhou ◽  
Wen Fang Huang
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Control Method ◽  
Optimization Method ◽  
Separation Process ◽  
Control Effect ◽  
Environment Change ◽  
Good Convergence ◽  
Single Neuron Pid

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

scholarly journals Research on Tracking Control of A Quadrotor UAV Based on Model Predictive Control and PD Control

2021 ◽  
Vol 2085 (1) ◽  
pp. 012008
Author(s):  
Jimin Yu ◽  
Zhi Yong ◽  
Yousi Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Algorithm ◽  
Control Method ◽  
Control Input ◽  
Pd Control ◽  
Quadrotor Uav ◽  
Model Predictive Control Algorithm ◽  
Method Model

Abstract In order to solve the problem of path tracking of a quadrotor UAV, this paper proposes a track tracking control method which combines Model Predictive Control algorithm and PD control method. Model Predictive Control algorithm can generate control input for formation flight and track the specified trajectory. PD control can achieve rapid response to attitude and adjust error quickly. The simulation results verify the effectiveness of the proposed control method.

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