1,196 research outputs found
The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators
Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft
A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles
This paper proposes a real-time nonlinear model
predictive control (NMPC) strategy for direct yaw moment control
(DYC) of distributed drive electric vehicles (DDEVs). The NMPC
strategy is based on a control-oriented model built by integrating
a single track vehicle model with the Magic Formula (MF) tire
model. To mitigate the NMPC computational cost, the
continuation/generalized minimal residual (C/GMRES) algorithm
is employed and modified for real-time optimization. Since the
traditional C/GMRES algorithm cannot directly solve the
inequality constraint problem, the external penalty method is
introduced to transform inequality constraints into an
equivalently unconstrained optimization problem. Based on the
Pontryagin’s minimum principle (PMP), the existence and
uniqueness for solution of the proposed C/GMRES algorithm are
proven. Additionally, to achieve fast initialization in C/GMRES
algorithm, the varying predictive duration is adopted so that the
analytic expressions of optimally initial solutions in C/GMRES
algorithm can be derived and gained. A Karush-Kuhn-Tucker
(KKT) condition based control allocation method distributes the
desired traction and yaw moment among four independent
motors. Numerical simulations are carried out by combining
CarSim and Matlab/Simulink to evaluate the effectiveness of the
proposed strategy. Results demonstrate that the real-time NMPC
strategy can achieve superior vehicle stability performance,
guarantee the given safety constraints, and significantly reduce the
computational efforts
A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy
This study presents an integrated hybrid solution to mandatory lane changing problem
to deal with accident avoidance by choosing a safe gap in highway driving. To manage
this, a comprehensive treatment to a lane change active safety design is proposed from
dynamics, control, and decision making aspects.
My effort first goes on driver behaviors and relating human reasoning of threat in
driving for modeling a decision making strategy. It consists of two main parts; threat assessment
in traffic participants, (TV s) states, and decision making. The first part utilizes
an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating
the traffic quantities. Then I propose a decision strategy, which is based on Markov
decision processes (MDPs) that abstract the traffic environment with a set of actions, transition
probabilities, and corresponding utility rewards. Further, the interactions of the TV s
are employed to set up a real traffic condition by using game theoretic approach. The question
to be addressed here is that how an autonomous vehicle optimally interacts with the
surrounding vehicles for a gap selection so that more effective performance of the overall
traffic flow can be captured. Finding a safe gap is performed via maximizing an objective
function among several candidates. A future prediction engine thus is embedded in the
design, which simulates and seeks for a solution such that the objective function is maximized
at each time step over a horizon. The combined system therefore forms a predictive
fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy
to avoid accidents for a given traffic environment. I show the effect of interactions in decision
making process by proposing both cooperative and non-cooperative Markov game
strategies for enhanced traffic safety and mobility. This level is called the higher level
controller. I further focus on generating a driver controller to complement the automated
car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The
success of the combined decision process and trajectory generation is evaluated with a set
of different traffic scenarios in dSPACE virtual driving environment.
Next, I consider designing an active front steering (AFS) and direct yaw moment control
(DYC) as the lower level controller that performs a lane change task with enhanced
handling performance in the presence of varying front and rear cornering stiffnesses. I propose
a new control scheme that integrates active front steering and the direct yaw moment
control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces
with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design
a linear parameter varying controller (LPV) for combined AFS and DYC to perform a
commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled
with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed
for both stability and tracking reference. Simulation study confirms that the performance
of the proposed methods is quite satisfactory
Dynamic Optimization Self-adaptive AI Controller for a Four-wheel Independent Drive Electric Rover
In this paper, a dynamic optimization self-adaptive controller for a four-wheel independent drive electric rover has been investigated to enhance the dynamic stability. The proposed self-adaptive AI controller is based on dynamic Fuzzy Logic (FL) control mechanism. The dynamic self-adaptive properties have been integrated into the proposed FL controller through a dynamically tuned Particle Swarm Optimization (PSO) mechanism. Nevertheless, the dynamic FL controller and the dynamic PSO mechanism has been synchronized together for every sampling instance k to obtain the optimum performance of the electric rover. In this electric rover, all the four wheels have a fixed orientation and each wheel powered by a 250-Watt Brushless Direct Current (BLDC) motor through separate gear ratio mechanisms to obtain the desired torque and angular velocity. Therefore, the steering mechanism was achieved in this rover through the proposed AI controller, which was based on the differential speed mechanism. However, this paper presents the control methodology and obtained test results related to straight road tests under different slippery road conditions. The rover test results show that on different slippery road conditions the proposed PSO based FL controller has maintained the wheel slip ratio of all the four wheels which was less than 0.35 approximately. Here, the translational speed has been limited to 40 km/hr approximately within its recorded top speed of 90 km/hr while maintaining the desired fix orientation
Stability Control of Triple Trailer Vehicles
While vehicle stability control is a well-established technology in the passenger car realm, it is still an area of active research for commercial vehicles as indicated by the recent notice of proposed rulemaking on commercial vehicle stability by the National Highway Traffic Safety Administration (NHTSA, 2012). The reasons that commercial vehicle electronic stability control (ESC) development has lagged passenger vehicle ESC include the fact that the industry is generally slow to adopt new technologies and that commercial vehicles are far more complex requiring adaptation of existing technology. From the controller theory perspective, current commercial vehicle stability systems are generally passenger car based ESC systems that have been modified to manage additional brakes (axles). They do not monitor the entire vehicle nor do they manage the entire vehicle as a system
A Computationally Efficient Path Following Control Strategy of Autonomous Electric Vehicles with Yaw Motion Stabilization
his paper proposes a computationally efficient path following control strategy of autonomous electric vehicles (AEVs) with yaw motion stabilization. First, the nonlinear control-oriented model including path following model, single track vehicle model, and Magic Formula tire model, are constructed. To handle the stability constraints with ease, the nonlinear model predictive control (NMPC) technique is applied for path following issue. Here NMPC control problem is reasonably established with the constraints of vehicle sideslip angle, yaw rate, steering angle, lateral position error, and Lyapunov stability. To mitigate the online calculation burden, the continuation/ generalized minimal residual (C/GMRES) algorithm is adopted. The deadzone penalty functions are employed for handling the inequality constraints and holding the smoothness of solution. Moreover, the varying predictive duration is utilized in this paper so as to fast gain the good initial solution by numerical algorithm. Finally, the simulation validations are carried out, which yields that the proposed strategy can achieve desirable path following and vehicle stability efficacy, while greatly reducing the computational burden compared with the NMPC controllers by active set algorithm or interior point algorithm
RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance
This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation
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