Acceleration Control of Powered Wheelchairs With Nil-Mode-Exciting Profiler Considering Vibration Characteristic of Human Body

2013 ◽  
Author(s):  
Masaki Takahashi ◽  
Kyohei Okugawa
Keyword(s):  
Control System ◽  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Feedforward Control ◽  
Human Head ◽  
Fast Response ◽  
Upper Body ◽  
Model Parameters ◽  
Control Input ◽  
Sampling Function

Recently, the number of people who need powered wheelchairs has been increasing due to aging society. Riding comfort is very important for people who use powered wheelchairs. In addition, wheelchairs must respond well to a velocity command signal with a joystick controller because collision must be avoided. The relation between ride comfort and fast response is a trade-off one. To solve these problems, a suitable reference torque signal should be designed. Thus, we propose a control system for powered wheelchairs that can reduce the vibration caused to the human head and upper body and achieve a fast response. Moreover, to guarantee robustness against parameter variations such as human weight and the friction of joints, a two-degrees-of-freedom control system that consists of feedforward and feedback controllers has been designed. We have designed a feedforward control input that uses the nil-mode-exciting (NME) profiler, which is called a “preshaping profiler”. This preshaping profiler has a low-pass-shaped frequency characteristic. Therefore, no residual vibrations are caused at a frequency higher than a certain frequency (the sampling function frequency). In this study, the sampling function frequency has been designed in consideration of both vibration and response. To improve robustness against the variation of model parameters such as weight and friction, we have designed a wheel velocity feedback control added to the feedforward control. To verify the effectiveness of the proposed method, several numerical simulations have been carried out.

NEURAL ADAPTIVE CONTROL OF NONLINEAR MULTIVARIABLE SYSTEMS WITH APPLICATION TO A CLASS OF INVERTED PENDULUMS

2002 ◽  
Vol 12 (05) ◽  
pp. 411-424
Author(s):  
SHOULING HE
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Base Point ◽  
Upper Body ◽  
Model Parameters ◽  
State Variables

In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots.

Enhancement of vehicle handling characteristics by suspension kinematic control

2001 ◽  
Vol 215 (2) ◽  
pp. 197-216 ◽  
Author(s):  
S H Lee ◽  
U K Lee ◽  
C S Han
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Lateral Acceleration ◽  
Vehicle Body ◽  
Control Input ◽  
Vehicle Handling ◽  
Kinematic Control ◽  
Front Suspension ◽  
Handling Characteristics ◽  
Suspension Geometry

In this paper, the enhancement of vehicle handling characteristics through the active kinematic control system (AKCS) is investigated. AKCS can improve the stability and ride comfort of a vehicle by automatically controlling suspension geometry in accordance with the running conditions of a vehicle. The variable roll centre suspension concept in a McPherson strut suspension is proposed, and lateral acceleration feedback control is derived to calculate the control input. The independent rear wheel steering system, which controls both rear wheels independently and actively, is also proposed. To achieve this, three suggested positions for controlling the suspension geometry are considered. The first position is between the mounting point of the lower arm of a McPherson front suspension and the vehicle body. The second position is between the mounting point of the strut and the vehicle body. The third position is between the mounting point of the lateral link of the multilink rear suspension and the vehicle body. In order to evaluate the handling performance, a 15 degrees of freedom full vehicle model is constructed using the commercial multibody analysis program ADAMS. The control inputs for integrated control of the front and rear suspensions are defined, and roll centre migration and vehicle behaviour are investigated. In step steering and double lane change manoeuvres, the simulation results demonstrate that integrated kinematic control can adjust the roll centre migration, by which the handling characteristics of the AKCS vehicle such as roll angle, lateral acceleration and yaw rate are much improved.

scholarly journals Non-Certainty-Equivalent Adaptive Control of a Nonlinear Aeroelastic System

2010 ◽  
Vol 56 (4) ◽  
pp. 463-471 ◽  
Author(s):  
Keum Lee ◽  
Sahjendra Singh
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Certainty Equivalent ◽  
Control Surface ◽  
Limit Cycle Oscillation ◽  
Stream Velocity ◽  
Model Parameters ◽  
Control Input ◽  
Parameter Uncertainties ◽  
Aeroelastic System

Non-Certainty-Equivalent Adaptive Control of a Nonlinear Aeroelastic SystemThe development of a non-certainty-equivalent adaptive control system for the control of a nonlinear aeroelastic system is the subject of this paper. The prototypical aeroelastic wing section considered here includes structural nonlinearity and a single control surface for the purpose of control. Its dynamical model has two-degree-of-freedom and describes the plunge and pitch motion. It is assumed that the model parameters (except the sign of one of the control input coefficients) are not known. The uncontrolled aeroelastic model exhibits limit cycle oscillation beyond a critical free-stream velocity. Based on the attractive manifold, and the immersion and invariance methodologies, a non-certainty-equivalent adaptive state variable feedback control law for the trajectory tracking of the pitch angle is derived. Using the Lyapunov analysis, asymptotic convergence of the state variables to the origin is established. It is shown that the trajectory of the system converges to a manifold. The special feature of the designed control system is that the closed-loop system asymptotically recovers the performance of a deterministic controller. This cannot happen if certainty-equivalent adaptive controllers are used. Simulation results are presented which show that the control system suppresses the oscillatory responses of the system in the presence of large parameter uncertainties.

Brain-Computer Interface Application in Robotic Gripper Control

2018 ◽  
Author(s):  
Briana Landavazo ◽  
Vidya K. Nandikolla
Keyword(s):  
Control System ◽  
Degrees Of Freedom ◽  
Brain Computer Interface ◽  
Research Work ◽  
Computer Interface ◽  
Data Sets ◽  
Control Input ◽  
Robotic Hand ◽  
Test Results ◽  
Eeg Data

In robotics research, the electroencephalograph (EEG) based brain-computer interface (BCI) as a control input has been used in designing prosthesis, wheelchairs and virtual navigation. The paper presents the research work on BCI development that communicates between an operator and a robotic gripping device. The control of a BCI robotic hand is broken down into two main subsystems. The first subsystem acquires a signal from the brain through the Emotiv EPOC EEG headset, extracts features and translates them into an input to the control system. The second subsystem incorporates kinematics and feedback from sensors, to control the multiple degrees of freedom used in the gripping device depending on the action specified by the higher-level BCI control. The BCI is trained to filter and extract features relating to the different hand motions from the data sets. Machine learning is used in conjunction with data filtering, feature extraction, and feature classification techniques to create a more accurate and personalized BCI hand control system. The system analyzes the EEG data, compares with the EEG data patterns from previous attempts. The test results demonstrate the movement functions of the gripper using the BCI, and the success rate for each function are presented in this paper.

A suspension system with a variable roll centre for the improvement of vehicle handling characteristics

2001 ◽  
Vol 215 (6) ◽  
pp. 677-696 ◽  
Author(s):  
U Lee ◽  
C Han
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Suspension System ◽  
Lateral Acceleration ◽  
Vehicle Body ◽  
Control Input ◽  
Vehicle Handling ◽  
Body Side ◽  
Handling Characteristics ◽  
Suspension Geometry

In this paper, the improvement of vehicle handling characteristics using variable roll centre suspension (VRCS) is investigated. A vehicle with VRCS can improve stability and ride comfort by automatically controlling suspension geometry in accordance with the running conditions of the vehicle. To achieve this, a variable roll centre concept in the McPherson strut suspension system is suggested, while the two parts most sensitive for controlling the roll centre are suggested. One is between the vehicle body-side connecting portion of the lower arm and the vehicle body (control input, LCZ), and the other is between the vehicle body-side connecting portion of the strut and the vehicle body (control input, STY). Kinematic roll centre analysis, based on the analytic half-car model, shows that the use of two control inputs, LCZ and STY, can decrease migration of the roll centre and centre of gravity according to the side force. In order to quantify the relationship between roll centre and geometry control input and evaluate handling performance, a full vehicle model of 15 degrees of freedom (DOF) is constructed using multi-body dynamic analysis software, ADAMS. In step steering and double lane change manoeuvres, simulation results demonstrate that a vehicle with VRCS adjusts roll centre migration, and handling characteristics such as roll angle, lateral acceleration and yaw rate are much improved.

scholarly journals Performance Investigation of Integrated Model of Quarter Car Semi-Active Seat Suspension with Human Model

2020 ◽  
Vol 10 (9) ◽  
pp. 3185 ◽  
Author(s):  
Saransh Jain ◽  
Shubham Saboo ◽  
Catalin Iulian Pruncu ◽  
Deepak Rajendra Unune
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Supply Voltage ◽  
Mr Damper ◽  
Human Head ◽  
Integrated Model ◽  
Human Model ◽  
Heaviside Step Function ◽  
Seat Suspension ◽  
Quarter Car

In this paper, an integrated model of a semi-active seat suspension with a human model over a quarter is presented. The proposed eight-degrees of freedom (8-DOF) integrated model consists of 2-DOF for the quarter car model, 2-DOF for the semi-active seat suspension and 4-DOF for the human model. A magneto-rheological (MR) damper is implemented for the seat suspension. The fuzzy logic-based self-tuning (FLST) proportional–integral–derivative (PID) controller allows to regulate the controlled force on the basis of sprung mass velocity error and its derivative as input. The controlled force is tracked by the Heaviside step function which determines the supply voltage for the MR damper. The performance of the proposed integrated model is analysed, in-terms of human head accelerations, for several road profiles and at different speeds. The performance of the semi-active seat suspension is compared with the traditional passive seat suspension to validate the effectiveness of the proposed integrated model with a semi-active seat suspension. The simulation results show that the semi-active seat suspension improves the ride comfort significantly by reducing the head acceleration effectively compared to the passive seat suspension.

Direct Current Deadbeat Predictive Controller for BLDC Motor Using Single DC-Link Current Sensor

2020 ◽  
Vol 38 (8A) ◽  
pp. 1187-1199
Author(s):  
Qaed M. Ali ◽  
Mohammed M. Ezzalden
Keyword(s):  
Control System ◽  
High Speed ◽  
Fast Response ◽  
Bldc Motor ◽  
Current Controller ◽  
Three Phase ◽  
Predictive Controller ◽  
Two Phases ◽  
Dc Current ◽  
Three Phase Inverter

BLDC motors are characterized by electronic commutation, which is performed by using an electric three-phase inverter. The direct control system of the BLDC motor consists of double loops; including the inner-loop for current regulating and outer-loop for speed control. The operation of the current controller requires feedback of motor currents; the conventional current controller uses two current sensors on the ac side of the inverter to measure the currents of two phases, while the third current would be accordingly calculated. These two sensors should have the same characteristics, to achieve balanced current measurements. It should be noted that the sensitivity of these sensors changes with time. In the case of one sensor fails, both of them must be replaced. To overcome this problem, it is preferable to use one sensor instead of two. The proposed control system is based on a deadbeat predictive controller, which is used to regulate the DC current of the BLDC motor. Such a controller can be considered as digital controller mode, which has fast response, high precision and can be easily implemented with microprocessor. The proposed control system has been simulated using Matlab software, and the system is tested at a different operating condition such as low speed and high speed.

Control of coagulation process by dual wavelength article analyzer

1997 ◽  
Vol 36 (4) ◽  
pp. 135-142 ◽  
Author(s):  
Norihito Tambo ◽  
Yoshihiko Matsui ◽  
Ken-ichi Kurotani ◽  
Masakazu Kubota ◽  
Hirohide Akiyama ◽  
...  
Keyword(s):  
Water Quality ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Feedforward Control ◽  
Raw Water ◽  
Floc Size ◽  
Coagulation Process ◽  
Series Of Experiments ◽  
Size Data

A coagulation process for water purification plants mainly uses feedforward control based on raw water quality and empirical data and requires operator's help. We developed a new floc sensor for measuring floc size in a flush mixer to be used for floc control. A control system using model predictive control was developed on the floc size data. A series of experiments was performed to confirm controllability of settled water quality by controlling flush mixer floc size. An automatic control with feedback from the coagulation process was evaluated as practical and reliable. Finally this new control method was applied for actual plant and evaluated as practical.

Feasibility Study of the Novel Nitrification Control System with Feedback and Auto Calibration Feedforward Control

2015 ◽  
Vol 2015 (2) ◽  
pp. 1-5
Author(s):  
Ichiro Yamanoi ◽  
Yoshinori Nishida ◽  
Nobuyuki Nakamura ◽  
Takeshi Takemoto ◽  
Kenji Toyooka ◽  
...  
Keyword(s):  
Control System ◽  
Feasibility Study ◽  
Feedforward Control ◽  
The Novel
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