Longitudinal Motion Control Research Articles

Proportional-derivative linear quadratic regulator controller design for improved longitudinal motion control of unmanned aerial vehicles

This study investigates the longitudinal motion control of unmanned aerial vehicles through a simulation in MATLAB. The linear model of an unmanned aerial vehicle is applied to controllers to explicate the longitudinal motion of the unmanned aerial vehicle. The ideal performance for an unmanned aerial vehicle is to achieve the desired response instantly with 100% precision. However, currently available controllers need further improvements. Thus, a proportional-derivative linear quadratic regulator controller is developed and compared with a proportional-integral-derivative controller, a linear quadratic regulator controller, and a proportional linear quadratic regulator controller. The proportional-derivative linear quadratic regulator controller enhances the response of the system by reducing settling time by more than 95% compared with other available controllers. Additionally, the proportional-derivative linear quadratic regulator improves the root mean square error by almost 50% compared with the proportional linear quadratic regulator controller and improves rise time by almost 96% with reasonable overshoot.

Coordinated Control of Autonomous Four Wheel Drive Electric Vehicles for Platooning and Trajectory Tracking Using a Hierarchical Architecture

This paper presents a systematic method on how to design the coordinated lateral and longitudinal motion control system of autonomous four wheel drive (4WD) electric vehicles for platooning and trajectory tracking. First, mathematical models that perfectly describe the behaviors of autonomous 4WD vehicles are built-up, and the coupled effects in vehicle dynamic systems are given. Second, owing to the fact that autonomous vehicles are large-scale systems with strong coupling, nonlinearities, and uncertainties, a novel multi-objective hierarchical architecture used for coordinated lateral and longitudinal motion control is constructed, which is composed of a global cooperative control layer, a control allocation layer, and an action execution layer. A robust backstepping sliding mode controller (RBSMC) is presented in the cooperative control layer to provide the resultant forces/moment. The control allocation layer is designed using interior-point (IP) algorithm to determine the tire lateral and longitudinal forces, which result in the desired resultant forces/moment. The action execution layer consists of an inverse tire model, a slip ratio regulator for each wheel, and a slip angle regulator. Finally, simulation experiments are carried out under adverse driving conditions, and the results show that the proposed control architecture not only possesses excellent tracking performance but also enhances the riding comfort, stability, and safety of autonomous 4WD electric vehicles.

Simultaneous Estimation of Road Profile and Tire Road Friction for Automotive Vehicle

The longitudinal motion control of automotive vehicles is heavily reliant on information about the time-varying tire road friction coefficient. In the presence of varying road roughness profiles, the effective vertical load on each wheel varies dynamically, influencing the tire friction. In this paper, we integrated the vertical and longitudinal dynamics of a quarter wheel to form an integrated nonlinear model. In the modeled dynamics, the time-varying random road profile and the tire friction are treated as unknown inputs. To estimate these unknown inputs and states simultaneously, a combination of nonlinear Lipschitz observer and modified super-twisting algorithm (STA) observer is developed. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is established. Simulation results performed with the high-fidelity vehicle simulation software CarSim demonstrate the effectiveness of the proposed scheme in the estimation of states and unknown inputs.

Design of Longitudinal Motion Controller of a Small Unmanned Aerial Vehicle

The need for autonomous Unmanned Aerial Vehicles (UAVs) is very interesting nowadays. Autonomous UAVs provide the possibility of performing tasks and missions that are currently hazardous or can cost humans or money, enable autonomous search, persistent combat intelligence, surveillance and reconnaissance (ISR), and many other applications. This paper presents an overview of autopilot design with a detailed design of longitudinal autopilot of a Small Unmanned Aerial Vehicle (SUAV). The designed autopilot is applied to an Ultrastick-25e fixed wing UAV depending on longitudinal linear model and analytic linear model with trimmed values of straight and leveling scenario. The longitudinal motion controller design is started with the design of most inner loop (pitch rate feedback) of the longitudinal system, then pitch tracker design with a Proportional Integral (PI)controller. The guidance and control system is related with the design of altitude hold controller with Pcontroller as an example of outer loop controller design. The performance of two classic controller approaches for the design of autopilot are compared and evaluated for both linear and non-linear models. The proposed controller is chosen for design due to its higher performance than the classic one. At last the climbing turn scenario is applied to the whole autopilot (longitudinal and lateral) for the evaluation process. The results show a good performance in both disturbance rejection and robustness against sensors noise.

Lateral Handling Improvement with Dynamic Curvature Control for an Independent Rear Wheel Drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a newly-defined parameter, the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.

Enhanced Longitudinal Motion Control of UAV Simulation by Using P-LQR Method

This paper describes studies the longitudinal motion control of unmanned aerial vehicles (UAVs) by using a proportional-integral-derivative (PID) controller, linear-quadratic-regulator (LQR) controller, and P-LQR controller. The non-linear model of the motion is changed into a linear model and is implemented in the controllers to elucidate the longitudinal motion of UAVs. The control performance of the aircraft improves when the UAV reaches the desired response instantaneously and precisely. However, the performance of currently available controllers is not ideal, and a 10% improvement in controller performance can lead to a significant effect. Thus, the P-LQR controller is developed. This controller significantly improves the response times of the control system by at least 38.55% compared with other existing controllers. Hence, an alternative longitudinal motion control for UAVs has been proposed.

항공기 종 제어를 위한 Interval Type-2 퍼지논리 제어시스템

The flight control of aircraft, which has nonlinear time-varying dynamic characteristics depending on the various and unexpected external conditions, can be performed on two motions: longitudinal motion and lateral motion. In the longitudinal motion control of aircraft, pitch and trust are major control parameters and roll and yaw are control ones in the lateral motion control. Until now, a number of efficient and reliable control schemes that can guarantee the stability and maneuverability of the aircraft have been developed. Recently, the intelligent flight control scheme, which differs from the conventional control strategy requiring the various and complicate procedures such as the wind tunnel and environmental experiments, has attracted attention. In this paper, an intelligent longitudinal control scheme has been proposed utilizing Interval Type-2 fuzzy logic which can be recognized as a representative intelligent control methodology. The results will be verified through computer simulation with a F-4 jet fighter.

Aircraft longitudinal control without a pitch command in the autopilot

The laws of longitudinal motion control without data on the pitch angle are proposed. A procedure for analytical selection of the optimal gain in the elevator channel is proposed. The results of simulating the aircraft-autopilot system are presented.

Research on Robust Control of Longitudinal Motion of SWATH

Taking the uncertainty of SWATH longitudinal motion control system into account, the parameter uncertainty model of SWATH was built, and the -synthesis controller was designed to improve the longitudinal motion performance of SWATH. Then Matlab was utilized to simulate the controlled as well as uncontrolled longitudinal performance of SWATH. According to the simulation result, under different sailing conditions, the longitudinal motion performance of SWATH is still excellent. Therefore, the parameter uncertainty mathematical model built in this paper as well as the designed -synthesis controller are effective and applicable to improve the longitudinal motion performance and the robust performance of SWATH, providing the theoretical basis for the research on SWATH with high performance.

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are (1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control;(2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and (3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties as well as severe and adverse driving conditions.

Control systems of moving objects at formation of estimates of dynamic properties of flying vehicles in longitudinal motion

The problem of synthesis of control of longitudinal motion of a flying vehicle under the action of step signal of increment of required normal overload is considered. The control is determined according to the required quality indices of transition processes. The problem is solved for essentially nonlinear dependences of aerodynamic characteristics of the flying vehicle with account of limit relations for the velocity and the elevator deflection angle. Examples of calculations of control processes for longitudinal motion are given and analyzed.

Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

Vehicle Longitudinal Motion Simulation by Analytical Automatic Machines

In work examines an aspect of the human-machine system "Automobile-Environment-Driver" model construction, associated with a description of the vehicle longitudinal motion control systems of, allowing to reproduce controlled automobile motion under normalized conditions with various functionals of qualities.

Reinforcement learning of dynamic collaborative driving Part I: longitudinal adaptive control

Dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. Each vehicle involved is a composite non-linear system powered by an internal combustion engine, equipped with automatic transmission, rolling on rubber tyres with a hydraulic braking system. This paper focuses on the problem of longitudinal motion control. A longitudinal vehicle model is introduced which serves as the control system design platform. A longitudinal adaptive control system that uses Monte Carlo Reinforcement Learning (RL) is introduced. The results of the RL phase and the performance of the adaptive control system for a single automobile, as well as the performance in a multi-vehicle platoon, are presented.

Automata control systems

A framework is developed for the control design and stability analysis of state-feedback systems made out of automaton–controller pairs, here referred to as automata control systems. A single theorem, based on the Bellman–Ford algorithm, provides the conditions for the design of the controllers that make a given automaton optimal and stable. The automata approximation of continuous state-space models is also developed for the design of state-feedback controllers that can drive continuous plants. The approximation of continuous plants through automata makes the design of state-feedback controllers independent of the state-space description. No distinction is made in the treatment of linear and nonlinear plants. Controller synthesis and specification of the domains of attraction for the resulting plant–controller pair are systematically obtained for continuous time-invariant state-space models. The application of this framework for the stability analysis of the exact model of a digital filter is presented. The automata approximation is applied to design a single controller that stabilises a forced pendulum around two equilibria. The design of switching controllers using automata approximation is also developed and applied to the longitudinal motion control of an aircraft.

Gain-Scheduled Stabilization Control of a Unicycle Robot

In this paper, the posture stabilization and longitudinal motion control of a unicycle robot is presented. This unicycle robot has two gyroscopes acting as an actuator for the lateral stabilization and a wheel as an actuator for the longitudinal stabilization. The system is modeled based on Lagrangian Dynamics and system identification method. A gain-scheduled robust control method is applied to enhance its disturbance attenuation ability so as to achieve better performance in posture stabilization and longitudinal motion control. Satisfactory experimental results are obtained.

On the design of a longitudinal motion control system of a fully-submerged hydrofoil craft based on the optimal preview servo system

The current control system of a fully-submerged hydrofoil craft based on optimal feedback control has several problems: it has no good performance of contouring waves and it cannot reduce the effect of wave disturbance. In this paper, we apply the optimal preview servo system to the longitudinal control system of a fully-submerged hydrofoil craft in order to settle the problems of current control system. The control system based on optimal preview servo system is composed of feedback controller and feedforward controller. Also in order to design the control system, the necessary prediction range of future information, the weight function in performance index, the future reference input and wave disturbance are determined. Finally, the validity of the proposed control system in this paper is confirmed by simulation. The simulation results show that the control system has good performance of contouring waves in following seas and is effective to settle the problems of current control system of a fully-submerged hydrofoil craft.

An experimental study of the longitudinal motion control of a fully submerged hydrofoil model in following seas

The seakeeping performance of a fully submerged hydrofoil craft suffers in following seas. It is well-known that the sluggishness of the hydrofoil control system is due to the orbital motion of the waves. This paper presents results of a wave-tank study of such a control system applied to a hydrofoil in following seas. The variation of the foil’s lift and angle of attack in regular waves are first studied. Then, the experimental model and its control system are re-designed, based on the data gathered in the first phase of the study. The re-design of the control system is based on regulator and Kalman filtering. Results obtained from tank tests of the re-designed system in following seas show that the sluggishness of the control system originated from the orbital motion of the waves.

The motion of wheeled robots

The equations of motion of three-wheeled robots with two drive wheels and one passive caster wheel are derived and investigated. The control of longitudinal motion and turns of such a robot is implemented by appropriate control of the independent motors of the drive wheels. The research is carried out under the assumption that the robot is moving on a horizontal plane surface and that the wheels do not slip. A system of two non-linear equations with two controls is obtained for the non-holonomic system considered. The dependence of the phase portrait on the values of the constant controls and parameters of the system, taking into account the asymmetry of the robot, is investigated. The results obtained are not only of theoretical but also of practical interest.

Longitudinal fuzzy control of a submerged vehicle

Implementation of classical control techniques for the control of a submerged vehicle's movement is considered very complicated, due to the complexity of the motion equations, governing the submerged body's motion (nonlinear 6 degrees of freedom equations), the strong interaction between body's axes and the poor effectiveness of the control surfaces, compared to the vehicle's inertia. However, human operators control the submerged vehicle successfully, utilizing a set of heuristic “thumb rules”, which they acquire through a long and expensive training phase. In this paper we investigate the applicability of fuzzy control in imitating the human operator, in order to achieve a simple and compact representation of the control rules. In order to simplify the problem, we consider a special case – longitudinal motion control. We will show that an efficient and robust controller can be obtained, utilizing the fuzzy control method. We will demonstrate the effectiveness of a simple predictor on human operator's performance, and consequently on fuzzy controller synthesis. Finally, we will investigate the fuzzy controller's performance and compare it to the performance of a conventional controller.