Data driven controller based on fuzzy rule adaptive network: with experimental validation

2019 ◽  
Vol 38 (6) ◽  
pp. 1782-1799
Author(s):  
Chidentree Treesatayapun
Keyword(s):  
Characteristic Curve ◽  
Tracking Error ◽  
Design Procedure ◽  
Output Characteristic ◽  
Data Driven ◽  
Superior Performance ◽  
Input Output ◽  
Output Data ◽  
Content Type ◽  
Experimental Systems

Purpose The purpose of this paper is to design an online-data driven adaptive control scheme based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems. Design/methodology/approach By using the input-output characteristic curve of controlled plant and the set of IF-THEN rules based on human knowledge inspiration, the adaptive controller is established by an adaptive FREN. The learning algorithm is established with convergence proof of the closed-loop system and controller’s parameters are directly designed by experimental data. Findings The convergence of tracking error is verified by the theoretical results and the experimental systems. The experimental systems and comparison results show that the proposed controller and its design procedure based on input-output data can achieve superior performance. Practical implications The theoretical aspect and experimental systems with the light-emitting diode (LED) current control and the robotic system prove that the proposed controller can be designed by using only input-output data of the controlled plants when the tracking error can be affirmed the convergence. Originality/value The proposed controller has been theoretically developed and used through experimental systems by using only input-output data of the controlled plant. The novel design procedure has been proposed by using the input-output characteristic curve for both positive and negative control directions.

Chaotic motion control of a vibro-impact system based on data-driven control method

2021 ◽  
pp. 107754632110433
Author(s):  
Xiao-juan Wei ◽  
Ning-zhou Li ◽  
Wang-cai Ding
Keyword(s):  
Motion Control ◽  
Data Model ◽  
Chaotic Motion ◽  
Control Method ◽  
Data Driven ◽  
Damping Coefficient ◽  
Input Output ◽  
Output Data ◽  
Controlled System ◽  
Impact System

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

scholarly journals KiwiSaver fund performance and asset allocation policy

2019 ◽  
Vol 31 (2) ◽  
pp. 232-257
Author(s):  
Huong Dieu Dang
Keyword(s):  
Asset Allocation ◽  
Tracking Error ◽  
Excess Return ◽  
Superior Performance ◽  
Data Set ◽  
Content Type ◽  
Allocation Policy ◽  
Holding Period ◽  
Management Fees ◽  
The Impact

Purpose This paper aims to examine the performance and benchmark asset allocation policy of 70 KiwiSaver funds catergorised as growth, balanced or conservative over the period October 2007-June 2016. The study focuses on the sources for returns variability across time and returns variation among funds. Design/methodology/approach Each fund is benchmarked against a portfolio of eight indices representing eight invested asset classes. Three measures were used to examine the after-fee benchmark-adjusted performance of each fund: excess return, cumulative abnormal return and holding period returns difference. Tracking error and active share were used to capture manager’s benchmark deviation. Findings On average, funds underperform their respective benchmarks, with the mean quarterly excess return (after management fees) of −0.15 per cent (growth), −0.63 per cent (balanced) and −0.83 per cent (conservative). Benchmark returns variability, on average, explains 43-78 per cent of fund’s across-time returns variability, and this is primarily driven by fund’s exposures to global capital markets. Differences in benchmark policies, on average, account for 18.8-39.3 per cent of among-fund returns variation, while differences in fees and security selection may explain the rest. About 61 per cent of balanced and 47 per cent of Growth funds’ managers make selection bets against their benchmarks. There is no consistent evidence that more actively managed funds deliver higher after-fee risk-adjusted performance. Superior performance is often due to randomness. Originality/value This study makes use of a unique data set gathered directly from KiwiSaver managers and captures the long-term strategic asset allocation target which underlines the investment management process in reality. The study represents the first attempt to examine the impact of benchmark asset allocation policy on KiwiSaver fund’s returns variability across time and returns variation among funds.

Analytical, numerical and experimental investigation of a giant magnetostrictive (GM) force sensor

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Mojtaba Ghodsi ◽  
Shahed Mirzamohamadi ◽  
Soheil Talebian ◽  
Yousef Hojjat ◽  
Mohammadmorad Sheikhi ◽  
...  
Keyword(s):  
Optimum Condition ◽  
Numerical Simulations ◽  
Analytical Model ◽  
Characteristic Curve ◽  
Design Procedure ◽  
Force Sensor ◽  
Maximum Sensitivity ◽  
Experimental Setup ◽  
Content Type ◽  
Sensitivity Estimation

Purpose – This paper aims to investigate a novel giant magnetostrictive (GM) force sensor using Terfenol-D rod. Design/methodology/approach – First of all, principle of GM force sensor based on positive magnetostriction of Terfenol-D is presented. Then, design procedure of the GM force sensor is stated. Magnetic properties such as B-H curve and permeability of Terfenol-D are measured by a novel experimental setup and the results are used in analytical model, sensitivity estimation and numerical simulations. Then, an analytical model is presented and a numerical simulation using CST Studio Suite 2011 software is done. So as a result of numerical simulations, optimum geometry of the GM force sensor is obtained related to the condition in which the GM force sensor has highest sensitivity. After that, the sensor is fabricated using the simulation results and is tested by means of an experimental setup. Characteristic curve of the GM force sensor in several conditions is measured and the optimum operational condition is obtained considering highest sensitivity condition of the sensor. Also operational diagrams of the GM force sensor is plotted in loading and unloading conditions. Characteristics of the GM force sensor in optimum condition are presented. Findings – It was found that the GM force sensor has maximum sensitivity and maximum linearity in 0.8A current, which can be known as optimum condition of application. In this sensor, maximum sensitivity is 0.51 mV/N (while current is 0.8A), which is highest among older investigations. Originality/value – At last, theoretical, numerical and experimental results are compared and the criteria for magnetostrictive sensor design are presented.

Neural control of robot manipulators considering motor voltage saturation: performance evaluation and experimental validation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Izadbakhsh ◽  
Saeed Khorashadizadeh
Keyword(s):  
Performance Evaluation ◽  
Stability Analysis ◽  
Neural Control ◽  
Actuator Saturation ◽  
Tracking Error ◽  
Design Procedure ◽  
Content Type ◽  
Electrical Motors ◽  
The Stability ◽  
Voltage Saturation

Purpose This paper aims to design a neural controller based on radial basis function networks (RBFN) for electrically driven robots subjected to constrained inputs. Design/methodology/approach It is assumed that the electrical motors have limitations on the applied voltages from the controller. Due to the universal approximation property of RBFN, uncertainties including un-modeled dynamics and external disturbances are represented with this powerful neural network. Then, the lumped uncertainty including the nonlinearities imposed by actuator saturation is introduced and a mathematical model suitable for model-free control is presented. Based on the closed-loop equation, a Lyapunove function is defined and the stability analysis is performed. It is assumed that the electrical motors have limitations on the applied voltages from the controller. Findings A comparison with a similar controller shows the superiority of the proposed controller in reducing the tracking error. Experimental results on a SCARA manipulator actuated by permanent magnet DC motors have been presented to guarantee its successful practical implementation. Originality/value The novelty of this paper in comparison with previous related works is improving the stability analysis by involving the actuator saturation in the design procedure. It is assumed that the electrical motors have limitations on the applied voltages from the controller. Thus, a comprehensive approach is adopted to include the saturated and unsaturated areas, while in previous related works these areas are considered separately. Moreover, a performance evaluation has been carried out to verify satisfactory performance of transient response of the controller.

Development and motion control of spatial serial robotic manipulator under varying disturbances

2019 ◽  
Vol 16 (4) ◽  
pp. 460-467
Author(s):  
Alex Barre Epenetus ◽  
Meera CS ◽  
Santhakumar Mohan ◽  
Mukul Kumar Gupta
Keyword(s):  
Motion Control ◽  
Tracking Error ◽  
Robotic Manipulator ◽  
Superior Performance ◽  
Control Technique ◽  
Motion Controller ◽  
Content Type ◽  
Joint Friction ◽  
Dynamics Response ◽  
Torque Observer

Purpose Key challenges in evaluating the performance of a robotic manipulator are disturbances that rise internally and externally. Effects of non-linear disturbances like varying payload and joint friction can adversely affect the tracking performance in a robotic manipulator. This paper aims to discuss motion control of a three-link spatial manipulator using a computed torque observer-based control technique. Design/methodology/approach The overall motion control problem consists of derivation of kinematic and dynamic model of the manipulator followed by the control design to achieve desired manipulator response. In this study, the manipulator is subjected to uncertain varying load disturbances. The proposed motion controller compensates the effect of the disturbances and guarantees the convergence of tracking error to steady state value. Findings One major advantage of using observer-based control is positioning accuracy with robustness to parameter uncertainty and fast dynamics response. The performance of the proposed control technique is validated through real-time experiments conducted on the manipulator. The experiment results confirm the superior performance of the control system in achieving perfect tracking. Originality/value This paper demonstrates an observer-based control technique over a serial spatial manipulator which can be applied different robotic configurations under the effect of varying disturbances.

A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

2019 ◽  
Vol 41 (14) ◽  
pp. 3936-3942 ◽  
Author(s):  
Na Lin ◽  
Hao Liang ◽  
Yunkai Lv ◽  
Ronghu Chi
Keyword(s):  
Ethanol Fermentation ◽  
Iterative Learning Control ◽  
Fermentation Process ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Input Output ◽  
Forgetting Factor ◽  
Product Concentration

Ethanol fermentation process (EFP) is characterized as a repetitive batch process with strong nonlinear behavior, changing operational conditions and exogenous disturbances which causes huge cost and hard difficulties in modeling an EFP. In this work, a forgetting-factor based data-driven optimal terminal iterative learning control (FF-DDOTILC) is proposed for the product concentration control of an EFP, which is regarded as an unknown nonlinear and nonaffine discrete-time system in general. An iterative dynamic linearization method is introduced to transfer the nonlinear system equivalently into a linear parametric incremental input-output form. The learning control law is derived by iteratively optimizing the proposed new objective function with a forgetting-factor. Meanwhile, a project parameter updating law is designed to estimate the unknown parameters in the linear input-output data model. By introducing a forgetting-factor, the proposed method becomes more flexible and efficient with a better control performance. The proposed FF-DDOTILC only depends on the I/O data for the design and analysis where the convergence of tracking error is guaranteed mathematically. The proposed method is applicable and effective in the product concentration control of the ethanol fermentation process verified through detail simulations.

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