Motion Cueing Algorithms: A Review

2017 ◽  
Vol 1 (1) ◽  
pp. 90-106 ◽  
Author(s):  
Sergio Casas ◽  
Ricardo Olanda ◽  
Nilanjan Dey
Keyword(s):  
Predictive Control ◽  
Control Strategies ◽  
General Scheme ◽  
Level Control ◽  
Motion Platform ◽  
Motion Cues ◽  
Vehicle Simulation ◽  
Robotic Motion ◽  
Motion Cueing ◽  
High Level

Robotic motion platforms are commonly used in motion-based vehicle simulation. However, the reproduction of realistic accelerations within a reduced workspace is a major challenge. Thus, high-level control strategies commonly referred to as motion cueing algorithms (MCA) are required to convert the simulated vehicle physical state into actual motion for the motion platform. This paper reviews the most important strategies for the generation of motion cues in simulators, listing the advantages and drawbacks of the different solutions. The motion cueing problem, a general scheme and the four most common approaches – classical washout, adaptive washout, optimal control and model predictive control – are presented. The existing surveys of the state-of-the-art on motion cueing are usually limited to list the MCA or to a particular vehicle application. In this work, a comprehensive vehicle-agnostic review is presented. Moreover, evaluation and tuning of MCA are also considered, classifying the different methods, and providing examples of each class.

Actuator Constrained Motion Cueing Algorithm for a Redundantly Actuated Stewart Platform

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Justin Pradipta ◽  
Oliver Sawodny
Keyword(s):  
Optimization Problem ◽  
Flight Simulation ◽  
Constrained Motion ◽  
Motion Platform ◽  
Case Scenario ◽  
Worst Case ◽  
Motion Cues ◽  
Motion Cueing ◽  
Redundantly Actuated ◽  
Relationship Of

An improved method to provide a motion trajectory for full flight simulator to simulate the acceleration during a flight simulation is presented. The motion cueing trajectory is based on a constrained optimization problem, with the generated optimal acceleration cues subjected to the actuators travel constraints of the motion platform. The motion platform researched in this contribution is a redundantly actuated parallel manipulator, therefore the available workspace is more limited and the actuator constraints become more complex. The differential kinematic analysis is utilized in the optimization problem to define the relationship of the acceleration in the platform coordinate and in the actuator coordinates. An acceleration profile is defined in function of the actuator travel to create a strict acceleration constraint in the actuator coordinate, thus a strict travel constraint. The algorithm is tested in a simulation and implemented in a full size redundantly actuated motion platform. Measurement results show that the proposed new motion cueing algorithm (MCA) is able to keep the actuators within their travel limit and at the same time provide the correct motion cues for the simulator pilots. The need to tune the MCA for the worst case scenario which is necessary to avoid damage to the platform, while at the same time can be disadvantageous for the normal case use, is relieved by the utilization of the online optimization process.

Powered Ankle-Foot Prostheses: A Survey on Sensing Systems and Control Strategies

2019 ◽  
Author(s):  
Erik Chumacero-Polanco ◽  
James Yang
Keyword(s):  
Control Strategies ◽  
Level Control ◽  
Low Level ◽  
Kinetic Information ◽  
Sensing Systems ◽  
Systems And Control ◽  
Finite State ◽  
High Level ◽  
And Control

Abstract People who have suffered a transtibial amputation show diminished ambulation and impaired quality of life. Powered ankle foot prostheses (AFP) are used to recover some mobility of transtibial amputees (TTAs). Powered AFP is an emerging technology that has great potential to improve the quality of life of TTAs with important avenues for research and development in different fields. This paper presents a survey on sensing systems and control strategies applied to powered AFPs. Sensing kinematic and kinetic information in powered AFPs is critical for control. Ankle angle position is commonly obtained via potentiometers and encoders directly installed on the joint, velocities can be estimated using numerical differentiators, and accelerations are normally obtained via inertial measurement units (IMUs). On the other hand, kinetic information is usually obtained via strain gauges and torque sensors. On the other hand, control strategies are classified as high- and low-level control. The high-level control sets the torque or position references based on pattern generators, user’s intent of motion recognition, or finite-state machine. The low-level control usually consists of linear controllers that drive the ankle’s joint position, velocity, or torque to follow an imposed reference signal. The most widely used control strategy is the one based on finite-state machines for the high-level control combined with a proportional-derivative torque control for low-level. Most designs have been experimentally assessed with acceptable results in terms of walking speed. However, some drawbacks related to powered AFP’s weight and autonomy remain to be overcome. Future research should be focused on reducing powered AFP size and weight, increasing energy efficiency, and improving both the high- and the low-level controllers in terms of efficiency and performance.

scholarly journals MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

Vehicles ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 625-647
Author(s):  
Yash Raj Khusro ◽  
Yanggu Zheng ◽  
Marco Grottoli ◽  
Barys Shyrokau
Keyword(s):  
Driving Simulator ◽  
Motion Platform ◽  
Driver Training ◽  
Human Machine Interaction ◽  
Reference Tracking ◽  
Motion Cues ◽  
Adaptive Weights ◽  
Washout Filter ◽  
Motion Cueing ◽  
And Performance

Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is nonlinear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and use maximum workspace. Furthermore, adaptive weights-based tuning is used to smooth the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace use.

scholarly journals Multi-Objective Supervisory Control for DC/DC Converters in Advanced Aeronautic Applications

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3216 ◽  
Author(s):  
Alberto Cavallo ◽  
Giacomo Canciello ◽  
Beniamino Guida ◽  
Ponggorn Kulsangcharoen ◽  
Seang Yeoh ◽  
...  
Keyword(s):  
Low Voltage ◽  
Control Strategies ◽  
Gain Control ◽  
High Gain ◽  
Level Control ◽  
Mathematical Arguments ◽  
Control Scheme ◽  
Sliding Manifold ◽  
High Gain Control ◽  
High Level

In this paper, an intelligent control strategy for DC/DC converters is proposed. The converter connects two DC busses, a high-voltage and a low-voltage bus. The control scheme is composed by a two-layer architecture, a low-level control based on the concept of sliding manifold, and high gain control, and a high-level control used to guarantee the achievement of various objectives. The proposed control strategies are based on solid mathematical arguments, with stability proofs for the non-linear case, and decision trees for parameter selection. The paper results are analyzed and discussed by using simulation at different detail levels in MATLAB/Stateflow/PowerSystem, and validated by experimental results, also considering MIL standard performance indices.

scholarly journals MPC-Based Motion Cueing Algorithm for a 6 DOF Driving Simulator with Actuator Constraints

2020 ◽  
Author(s):  
Yash Raj Khusro ◽  
Yanggu Zheng ◽  
Marco Grottoli ◽  
Barys Shyrokau
Keyword(s):  
Driving Simulator ◽  
Motion Platform ◽  
Driver Training ◽  
Human Machine Interaction ◽  
Reference Tracking ◽  
Motion Cues ◽  
Adaptive Weights ◽  
Washout Filter ◽  
Motion Cueing ◽  
And Performance

Driving simulators are widely used for understanding human-machine interaction, driver behavior and in driver training. The effectiveness of simulators in these process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

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