EVALUATION OF DISCRETE ADAPTIVE FAIL-SAFE ACTIVE SUSPENSION

1997 ◽  
Vol 21 (3) ◽  
pp. 205-220 ◽  
Author(s):  
R.V. Dukkipati ◽  
S.S. Vallurupalli ◽  
M.O.M. Osman
Keyword(s):  
Adaptive Control ◽  
Active Suspension ◽  
Full Scale ◽  
Least Square ◽  
Recursive Least Square ◽  
Single Degree Of Freedom ◽  
Computer Controlled ◽  
Fail Safe ◽  
Model Reference Adaptive ◽  
First Time

Hardware implementation of discrete adaptive control for a full scale vehicular single degree of freedom (SDOF) active suspension has been discussed in this paper. This paper describes an experimental evaluation of full scale fail-safe adaptive active (SDOF) suspension system that has been performed for the first time. A servo hydraulic force actuator is installed along with passive suspension components to form a fail-safe active suspension. A discrete model reference adaptive control (DMRAC) approach with recursive least square (RLS) estimation and covariance modification has been used for the software/hardware based digital control. A real time computer controlled adaptive active suspension software which shows the experimental response and animation of the results has been developed.

Resolved Motion Adaptive Control for Mechanical Manipulators

1984 ◽  
Vol 106 (2) ◽  
pp. 134-142 ◽  
Author(s):  
C. S. G. Lee ◽  
B. H. Lee
Keyword(s):  
Adaptive Control ◽  
Low Cost ◽  
Equations Of Motion ◽  
Least Square ◽  
Hand Position ◽  
Recursive Least Square ◽  
Joint Torques ◽  
Hand Trajectory ◽  
Self Tuning ◽  
Mechanical Manipulators

This paper presents the development of a resolved motion adaptive control which adopts the ideas of “resolved motion rate control” [8] and “resolved motion acceleration control” [10] to control a manipulator in Cartesian coordinates for various loading conditions. The proposed adaptive control is performed at the hand level and is based on the linearized perturbation system along a desired hand trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component resolves the specified positions, velocities, and accelerations of the hand into a set of values of joint positions, velocities, and accelerations from which the nominal joint torques are computed using the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least square identification scheme and an optimal adaptive self-tuning controller for the linearized system computes the perturbation torques which reduce the manipulator hand position and velocity errors along the nominal hand trajectory. The feasibility of implementing the proposed adaptive control using present day low-cost microprocessors is explored.

scholarly journals Fractional order adaptive MRAC controller for an active suspension system

2020 ◽  
Vol 5 (2) ◽  
pp. 112-117
Author(s):  
SEIF EDDINE KHELAS ◽  
SAMIR LADACI ◽  
YASSINE BENSAFIA
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Order Parameter ◽  
Active Suspension ◽  
Adaptive Controller ◽  
Suspension System ◽  
Conventional Model ◽  
Control Schemes ◽  
Classical Control ◽  
Model Reference Adaptive

This paper investigates the use of fractional order operators in conventional model reference adaptive control (MRAC). A fractional adaptive controller is designed based on the use of a fractional-order parameter adjustment rule. Applied in numerical simulations for an active suspension system and compared with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

Model Reference Adaptive Control of HMB Based on PSO-LS-SVM Inverse

2013 ◽  
Vol 416-417 ◽  
pp. 870-875
Author(s):  
Xian Xing Liu ◽  
Jie Chen ◽  
Yi Du ◽  
Kai Shi
Keyword(s):  
Adaptive Control ◽  
High Precision ◽  
Pso Algorithm ◽  
Model Reference Adaptive Control ◽  
Magnetic Bearing ◽  
Inverse Model ◽  
Least Square ◽  
Support Vector ◽  
Model Reference ◽  
Model Reference Adaptive

To realize the hybrid magnetic bearing (HMB) nonlinear decoupling control with high precision, a strategy of model reference adaptive control (MRAC) based on the least square support vector machine (LS-SVM) inverse is proposed. After analyzing the reversibility of HMB, the LS-SVM regression theory is used to identify the inverse model, the parameters of LS-SVM are optimized by Particle Swarm Optimization (PSO) algorithm. Then the nonlinear system is transformed into a pseudo-linear system by connecting the optimized the inverse model and the original unit. MRAC is designed to realize the compound linear control for HMB. Simulation results confirm that the identified inverse model has high precision and the compound control strategy has good performance.

scholarly journals Parameter Identification of Inverter-Fed Induction Motors: A Review

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2194 ◽  
Author(s):  
Jing Tang ◽  
Yongheng Yang ◽  
Frede Blaabjerg ◽  
Jie Chen ◽  
Lijun Diao ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Induction Motor ◽  
High Performance ◽  
Least Square ◽  
Recursive Least Square ◽  
Identification Methods ◽  
Voltage Signal ◽  
High Frequency Ac ◽  
Identification Techniques ◽  
Model Reference Adaptive

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

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