MODIFIED-PID CONTROL WITH FEEDFORWARD IMPROVEMENT FOR 1-DEGREE-OF-FREEDOM PNEUMATIC MUSCLE ACTUATED SYSTEM

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Shin-Horng Chong ◽  
Chun-Yuan Chan ◽  
Kaiji Sato ◽  
Vasanthan Sakthivelu ◽  
Ser-Lee Loh
Keyword(s):  
Pid Control ◽  
Nonlinear Function ◽  
Design Procedure ◽  
Deep Understanding ◽  
Settling Time ◽  
Model Parameters ◽  
Nonlinear Properties ◽  
Highly Nonlinear ◽  
Advanced Controls ◽  
Point To Point

attention due to the favorable advantages that PMA has to offer such as inherent compliant safety, compactness, dust-resistant and powerful, especially for rehabilitation application. However, the highly non-linear phenomenon exhibited by PMA poses a challenge in positioning control of the mechanism. Due to the highly nonlinear properties of the PMA system, it is difficult and challengeable to model the system accurately. Many advanced controls have been proposed, however, majority of them requires accurate model parameters for the design and/ or deep understanding of control theory. Therefore, this research aims to highlight a practical and simple control framework capable of providing ameliorated compensation towards the non-linearities in a PMA positioning system. The proposed controller is a combination of a modified PID control incorporated with a model-based feed-forward element. The modified PID control is cascaded with a modeled-nonlinear function and a linearizer that works to compensate the influence of the nonlinearities. The design procedure of the proposed control remains simple and none of the known parameter is required. The proposed controller is verified experimentally using the constructed testbed – 1DOF PMA system; in point-to-point motion that driving in several step heights (5 mm, 10 mm, 20 mm, and 30 mm). At the step height of 30 mm, the proposed control has demonstrated three times smaller of overshoot and the reduction of 39% of settling time as compared with the conventional PID control. Overall, the experimental results show that the proposed controller is capable of demonstrating a satisfactory transient, with better overshoot reduction characteristic and faster settling time; and robust performance under default and in the presence of the change of load, in comparison with the conventional PID control. 

scholarly journals Enhanced Nonlinear PID Controller for Positioning Control of Maglev System

2021 ◽  
Author(s):  
Shin-Horng Chong ◽  
Roong-Soon Allan Chan ◽  
Norhaslinda Hasim
Keyword(s):  
Transient Response ◽  
Magnetic Levitation ◽  
Feedforward Control ◽  
Design Procedure ◽  
Deep Understanding ◽  
Model Parameters ◽  
Pd Controller ◽  
Positioning Control ◽  
Nonlinear Pid Controller ◽  
Magnetic Levitation System

Magnetic levitation (maglev) is a way of using electromagnetic fields to levitate objects without any noise or the need for petrol or air. Due to its highly nonlinear and unstable behavior, numerous control solutions have been proposed to overcome it. However, most of them still acquire precise dynamic model parameters, or deep understanding of control theory. To account the complexity in the design procedure, a practical controller consists of classical and modern control approaches are proposed. This chapter presents a practical controller for high positioning performance of a magnetic levitation system. Three strategies of the proposed controller where the PI-PD controller is to enhance transient response, the model-based feedforward control (FF) is incorporated with the PI-PD controller to enhance the overshoot reduction characteristic in attaining a better transient response, and lastly the disturbance compensator (Kz) is integrated as an additional feedback element to reduce the sensitivity function magnitude for robustness enhancement. The proposed controller - FF PI-PD + Kz has a simple and straightforward design procedure. The usefulness of the proposed controller is evaluated experimentally.

scholarly journals Product Replacement Policy in a Production Inventory Model with Replacement Period-, Stock-, and Price-Dependent Demand

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Subhajit Das ◽  
Amalesh Kumar Manna ◽  
Emad E. Mahmoud ◽  
Kholod M. Abualnaja ◽  
Abdel-Haleem Abdel-Aty ◽  
...  
Keyword(s):  
Nonlinear Function ◽  
Inventory Model ◽  
Replacement Policy ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Selling Price ◽  
Proposed Model ◽  
Highly Nonlinear ◽  
Production Inventory ◽  
Market Situation

In the competitive market situation, several companies confer various types of incentives and facilities during product sell to their customers with certain terms and conditions. For the products such as mobile, TV, water purifiers, marshal products, and many more, its corresponding companies offer replacement facility during the guarantee period to enhance the customers’ demand. In this study, we have formulated a production inventory model with considering product’s replacement facility of the failure product within guarantee periods to their customers. This work also leads two vital assumptions: (i) customers’ demand is depending on the replacement period, stock level, and selling price of the product and (ii) the rate of replacement loss of manufacturer’s capital is dependent on the replacement period, and it is a nonlinear function. Since the corresponding optimization problem is highly nonlinear, we have solved it by MATHEMATICA software. The concavity of the centre of interval-valued average profit of the proposed model is shown graphically. In order to justify the validity of the proposed model, a numerical example is considered and solved. Finally, the sensitivity analyses are carried out with respect to the different model parameters.

The Simulation Study for Auto Electrical Seat Controller Based on Fuzzy Control Technology

2011 ◽  
Vol 383-390 ◽  
pp. 7328-7331
Author(s):  
Lan Jiang Zhang ◽  
Gui Jie Wang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Effective Means ◽  
Nonlinear Function ◽  
Control Technology ◽  
Highly Nonlinear ◽  
Adjustment System ◽  
The Mathematical Model ◽  
Seat Adjustment ◽  
Controlled Object

Designed the control system policy for automobile electric seat using fuzzy control technology, therefore established its control model by Fuzzy Logic Toolbox, and carried on the off-line simulation to choose controller's optimum control parameters. From the dynamic viewpoint, the auto electric seat adjustment system is not only a complex nonlinear function which includes the location of the DC servo motor and the speed, but also contains serious nonlinear coupling interference, so the system is a highly nonlinear strong coupling, variable multivariable system. Application of traditional control methods (such as traditional PID) is difficult to meet its order requirements, so the research is highly robust method of intelligent control is an effective way to solve the problem. Fuzzy control technology has become the field in which drawn greater attention and researched in recent years. It doesn’t depend on the mathematical model of controlled object, has a good robustness, and nonlinear control characteristics, so it is an effective means to control the object with time-varying, non- linear parameters. In this paper, fuzzy control technology to achieve the orders of auto electric seat adjustment control system functions in the Literature [1], and the tracking of the system was simulated.

Comprehensive global sensitivity analysis of a repository model using different types of transformations and metamodeling techniques

2021 ◽  
Author(s):  
Sabine M. Spiessl ◽  
Dirk-A. Becker ◽  
Sergei Kucherenko
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Higher Order ◽  
Model Parameters ◽  
High Dimensional Model Representation ◽  
Model Output ◽  
Global Sensitivity ◽  
Sparse Polynomial ◽  
Sensitivity Indices ◽  
Highly Nonlinear

<p>Due to their highly nonlinear, non-monotonic or even discontinuous behavior, sensitivity analysis of final repository models can be a demanding task. Most of the output of repository models is typically distributed over several orders of magnitude and highly skewed. Many values of a probabilistic investigation are very low or even zero. Although this is desirable in view of repository safety it can distort the evidence of sensitivity analysis. For the safety assessment of the system, the highest values of outputs are mainly essential and if those are only a few, their dependence on specific parameters may appear insignificant. By applying a transformation, different model output values are differently weighed, according to their magnitude, in sensitivity analysis. Probabilistic methods of higher-order sensitivity analysis, applied on appropriately transformed model output values, provide a possibility for more robust identification of relevant parameters and their interactions. This type of sensitivity analysis is typically done by decomposing the total unconditional variance of the model output into partial variances corresponding to different terms in the ANOVA decomposition. From this, sensitivity indices of increasing order can be computed. The key indices used most often are the first-order index (SI1) and the total-order index (SIT). SI1 refers to the individual impact of one parameter on the model and SIT represents the total effect of one parameter on the output in interactions with all other parameters. The second-order sensitivity indices (SI2) describe the interactions between two model parameters.</p><p>In this work global sensitivity analysis has been performed with three different kinds of output transformations (log, shifted and Box-Cox transformation) and two metamodeling approaches, namely the Random-Sampling High Dimensional Model Representation (RS-HDMR) [1] and the Bayesian Sparse PCE (BSPCE) [2] approaches. Both approaches are implemented in the SobolGSA software [3, 4] which was used in this work. We analyzed the time-dependent output with two approaches for sensitivity analysis, i.e., the pointwise and generalized approaches. With the pointwise approach, the output at each time step is analyzed independently. The generalized approach considers averaged output contributions at all previous time steps in the analysis of the current step. Obtained results indicate that robustness can be improved by using appropriate transformations and choice of coefficients for the transformation and the metamodel.</p><p>[1] M. Zuniga, S. Kucherenko, N. Shah (2013). Metamodelling with independent and dependent inputs. Computer Physics Communications, 184 (6): 1570-1580.</p><p>[2] Q. Shao, A. Younes, M. Fahs, T.A. Mara (2017). Bayesian sparse polynomial chaos expansion for global sensitivity analysis. Computer Methods in Applied Mechanics and Engineering, 318: 474-496.</p><p>[3] S. M. Spiessl, S. Kucherenko, D.-A. Becker, O. Zaccheus (2018). Higher-order sensitivity analysis of a final repository model with discontinuous behaviour. Reliability Engineering and System Safety, doi: https://doi.org/10.1016/j.ress.2018.12.004.</p><p>[4] SobolGSA software (2021). User manual https://www.imperial.ac.uk/process-systems-engineering/research/free-software/sobolgsa-software/.</p>

A Novel Method to Improve the Accuracy of Grey Prediction Model in Economic Prediction

2013 ◽  
Vol 347-350 ◽  
pp. 3331-3335
Author(s):  
Qian Ru Wang ◽  
Xi Wei Chen ◽  
Da Shi Luo ◽  
Yu Feng Wei ◽  
Li Ya Jin ◽  
...  
Keyword(s):  
System Theory ◽  
Time Series Data ◽  
Grey System Theory ◽  
Series Data ◽  
Model Parameters ◽  
Economic Data ◽  
Grey System ◽  
Forecasting Accuracy ◽  
Highly Nonlinear ◽  
The Impact

Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular and non-stationary. Many models based on grey system theory could adapt to various economic time series data. However, some of these models didnt consider the impact of the model parameters, or only considered a simple change of the model parameters for the prediction. In this paper, we proposed the PSO based GM (1, 1) model using the optimized parameters in order to improve the forecasting accuracy. The experiment shows that PSO based GM (1, 1) gets much better forecasting accuracy compared with other widely used grey models on the actual chaotic economic data.

Differentially Flat Designs of Underactuated Mobile Manipulators

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Ji-Chul Ryu ◽  
Vivek Sangwan ◽  
Sunil K. Agrawal
Keyword(s):  
State Space ◽  
Controller Design ◽  
The State ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Combined System ◽  
Mobile Vehicle ◽  
Highly Nonlinear ◽  
Point To Point ◽  
Challenging Question

This paper presents a methodology for design of mobile vehicles, mounted with underactuated manipulators operating in a horizontal plane, such that the combined system is differentially flat. A challenging question of how to perform point-to-point motions in the state space of such a highly nonlinear system, in spite of the absence of some actuators in the arm, is answered in this paper. We show that, by appropriate inertia distribution of the links and addition of torsion springs at the joints, a range of underactuated designs is possible, where the underactuated mobile manipulator system is differentially flat. The differential flatness property allows one to efficiently solve the problem of trajectory planning and feedback controller design for point-to-point motions in the state space. The proposed method is illustrated by the example of a mobile vehicle with an underactuated three-link manipulator.

Topology Optimization for Piecewise Dynamic Models Using a Genetic Algorithm

2010 ◽  
Author(s):  
Yi Liu ◽  
Dragan Djurdjanovic
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Optimization Procedure ◽  
Nonlinear Dynamic System ◽  
Local Model ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Node Connectivity

It has been demonstrated in the previous research that the node connectivity in the graph encoding the topological neighborhood relationships between local models in a piecewise dynamic model may significantly affect the cooperative learning process. It was shown that a graph with a larger connectivity leads to a quicker learning adaption due to more rapidly decaying transients of the estimation of local model parameters. In the same time, it was shown that the accuracy could be degraded by a larger bias in the asymptotic portion of the estimations of local model parameters. The efforts in topology optimization should therefore strive towards a high accuracy of the asymptotic portion of the estimator of local model parameters while simultaneously accelerating the decay of the estimation transients. In this paper, we pursue minimization of the residual sum of squares of a piecewise dynamic model after a predetermined number of training steps. The optimization of inter-model topology is implemented via a genetic algorithm that manipulates adjacency matrices of the graph underlying the piecewise dynamic model. An example of applying the topology optimization procedure on a peicewise linear model of a highly nonlinear dynamic system is provided to show the efficacy of the new method.

PID PARAMETERS OPTIMIZATION USING PSO TECHNIQUE FOR NONLINEAR ELECTRO HYDRAULIC ACTUATOR

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Siti Marhainis Othman ◽  
Mohd Fua’ad Rahmat ◽  
Sahazati Md. Rozali ◽  
Sazilah Salleh
Keyword(s):  
Particle Swarm Optimization ◽  
Simulation Study ◽  
Pid Control ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Time Varying ◽  
Proportional Integral Derivative ◽  
Hydraulic Actuator ◽  
Swarm Optimization ◽  
Control Scheme

Electro-hydraulic actuator (EHA) system inherently suffers from uncertainties, nonlinearities and time- varying in its model parameters which cause the modeling and controller designs are more complicated. Proportional Integral Derivative (PID) control scheme has been proposed and the main problem with its application is to tune the parameters to its optimum values. This study will look into an optimization of PID parameters using particle swarm optimization (PSO). Simulation study has been done in Matlab and Simulink. 

scholarly journals Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

2019 ◽  
Vol 20 (13) ◽  
pp. 3216 ◽  
Author(s):  
Yu ◽  
Li ◽  
Li ◽  
Li ◽  
Li ◽  
...  
Keyword(s):  
Phenomenological Models ◽  
Optimization Problem ◽  
Fitness Function ◽  
Model Performance ◽  
Friction Model ◽  
Shaking Table ◽  
Model Parameters ◽  
Magnetorheological Elastomer ◽  
Highly Nonlinear ◽  
Hysteresis Response

Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

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