And for the stable flight control, a robust adaptive controller is designed for RHUG with unknown parameters and environmental disturbances. To use the robust control of TOT, the control input is designed for the buoyancy motor and thruster individually.
Introduction
Hybrid underwater glider
In [16] and [17], the design of a hybrid underwater glider called ZJU-HUG was presented with a rotatable thruster for an underwater docking mission. The design of coastal UG was presented and the study of the most important parameters to design the variable buoyancy system was carried out in [29].
Time-optimal trajectory
In this dissertation, a new stingray-shaped hybrid underwater glider (RHUG) hull design will be presented. And, to increase the net driving force, a new dual-drive motor design has been introduced.
Nonlinear control design
And then the experiment of TOT course control concept will be carried out using thrusters. A closed solution for the heave dynamics will be shown and validated by computer simulation.
Dynamics of RHUG
- Dynamics of underwater vehicles
- Design of RHUG platform
- Hull design
- Buoyancy engine and mass-shifter
- Battery
- Sensors
- Assembly
- Dynamics of RHUG
- Hydrodynamic coefficients
- Thruster modeling
- Buoyancy engine modeling
- Mass-shifter modeling
In the mass changer mechanism, the position of the moving mass is controlled by the DC motor. The analytical result of the force acting on the X and Z axes of the body-fixed frame is shown in Fig.
Time-optimal trajectory with actuator saturation for
Time-optimal trajectory
Heading motion
For the time-optimal problem, the disturbance is not used for the design of TOT trajectory and then Eq.
Analytic solution of heading dynamic equation
- Right-hand direction
- Left-hand direction
The angular trajectory can be formulated as (35) with the boundary condition as (36), and the angular acceleration trajectory can be obtained as (37). The constant 𝐶2 in the angle trajectory can be solved using the initial condition as shown in (77). The angle trajectory can be obtained as Eq. 101), by taking the integral of the angular velocity trajectory.
Finally, the angular acceleration trajectory can be obtained as (103) by taking the derivative of the angular velocity trajectory.
Time-optimal trajectory
The first segment of TOT starts at time 𝑡0 and ends at time 𝑡1 using the closed solutions of 𝜓𝑑1, 𝑟𝑑1 and 𝑟̇𝑑1.
Super-twisting sliding mode control
And the derivative of 𝑉 can be taken as the product of the sliding surface and its derivative, as shown in (126).
Computer simulation
- Simulation 1
- Simulation 2
- Simulation 3
21 TOT process and SMC without uncertainties. a) torque input, (b) angular velocity, (c) angular acceleration, and (d) angular curves as a function of time. This simulation will use the TOT trajectory with SMC's tracking controller with the parameter uncertainty and disturbance. 22 TOT process and SMC with uncertainties and disruptions. a) torque input, (b) velocity, (c) acceleration, and (d) angular position curves as a function of time.
23 Sliding surface in SMC as a function of velocity and position errors 3.6.3 Simulation 3. 24 TOT and ST-SMC trajectory with uncertainties and perturbations. a) input torque, (b) angular velocity, (c) angular acceleration, and (d) angular curves.
Time-optimal trajectory for heaving motion control using
Heave dynamics and TOT
In the RHUG system, the rate of buoyancy force can be expressed as 𝑐𝑚𝑎𝑥 for the rising speed and 𝑐𝑚𝑖𝑛 for the falling speed. Therefore, the buoyancy force driving the RHUG system during depth control is formulated as 𝑐𝑚𝑎𝑥(𝑡 − 𝑡0) and 𝑐𝑚𝑖𝑛(𝑡 − 𝛼). The first segment is from 𝑡0 to 𝑡1, the dynamics of this period can be referred to (131).
By solving all the dynamics below, it is possible to define the TOT trajectory of the RHUG system for a pure depth revolution.
Analytical solution of heave dynamics with buoyancy and
- First segment with positive rate
- Second segment with maximum input
- Third segment with constant velocity
- Fourth segment with negative rate
- Fifth segment with minimum input
And the acceleration path can be obtained by taking the derivative of the velocity path, as shown in (144). Using the derivative of the velocity path, the acceleration path of this segment can be obtained as (159). In the last segment, the thruster power will be used to make the vehicle achieve the desired depth in the shortest time.
And this dynamics solution is part of the work introduced in [33], and it will be rewritten as and (165) for velocity, acceleration, and position trajectory, respectively.
Time-optimal trajectory for depth motion
- Find 𝑧1, 𝑤1 and 𝑤1
- Find 𝑡2, 𝑧2, 𝑤2 and 𝑤2
- Find 𝑤3, 𝑧4 and 𝑤4
- Find 𝑧3, 𝑡3 and 𝑡4
- Find 𝛼 and 𝑡5
From the above, a set of constraints can be defined as (168) for the initial conditions of 𝑤0, 𝑤̇0 and 𝑧0. From this set, the constant 𝐶0 can be found and the ratio of 𝑎0 and 𝑎1 can be established. The boundary constants 𝐶1 and 𝐶2 can be calculated based on 𝑤1 as above and the given time 𝑡1.
Finally, the position trajectory at time 𝑡4 can be defined as (189) based on the distance 𝛥𝑧54.
Sliding mode control for heave dynamics
Then the time 𝑡4 is equal to the sum of the time 𝑡3 and the lift motor delay as shown in (193). In the control design, it is assumed that the hydrodynamic coefficients of the oscillation dynamics are known. 𝐾 = Δ𝑏𝑤2+ Δ𝑎|𝑤̇𝑑− 𝜆(𝑤 − 𝑤𝑑)| + 𝐷 + 𝜂𝑎𝑚𝑎𝑥 (201), where Δ𝑎 is the magnitude of the uncertainty of the parameter 𝑎, Δ𝑏 is the magnitude of the uncertainty of the parameter 𝑏, 𝐷 is the limit of the external disturbance 𝑑, 𝜂 is a small positive scalar and 𝑎𝑚𝑎𝑥 is the maximum possible 𝑎.
With the above SMC, the TOT tracking control can be robust to limited external disturbance.
Computer simulation
- Simulation 1
- Simulation 2
27, the actual value of the position, velocity and acceleration follow the TOT trajectory very well. And then all errors remain at zero at the end of the TOT trajectory in Fig. And the deviation from the actual input is equal to the magnitude of the disturbance.
And the size of the deviation in the control effort is equal to the limit of the disturbance.
Motivation
Composition of RHUG
The two side blocks are the left and right stroke engines, which will compress and expand the volume of air inside the cylinders. The middle block contains the control system and the moving mass in the mass changer mechanism. And the pitching control is used to adjust the pitch angle using a moving mass inside the mass shifter block.
Using the static model in (11), the thruster input signal with the required torque can be easily calculated with three linear functions.
Robust adaptive control for heading dynamics
To make the right-hand side negative definite, the control gain 𝑘4 must be designed as Using the condition in (215), we can prove that the derivative of the Lyapun function 𝑉2 is negative definite (N.D), as shown in (216).
Computer simulation
With the TOT concept in Chapter 2, the steering input will use maximum force and then minimum force. 41, but the control input of adaptive control is much smoother than sliding mode control, and can operate without knowledge of parameter boundaries. Furthermore, the larger the parameter bounds are, the larger the control input from SMC will be.
This adaptive control showed that the control input is in the range of predetermined limits between -10Nm to 10Nm while following the TOT trajectory.
Experiment
- First experiment with 𝑘1 = 2.5, 𝑘2 = 30
- Second experiment with 𝑘1 = 2, 𝑘2 = 30
- Third experiment with 𝑘1 = 2, 𝑘2 = 50
And when the TOT trajectory reaches the desired angle, the control input oscillates around the zero point. The control input is not perfectly rectangular, since the TOT concept due to the parameters used to design the TOT trajectory may not be the true values of the tested platform. However, the control input using the adaptive control is much smoother than that of the SMC control.
Before going to zero, the control input has a significant rise due to the overshoot in the position tracking.
Robust adaptive control design for vertical motion
Dynamics of vertical plane
There have been many studies on adaptive control for underwater vehicles over the past two decades. In these two papers, the authors designed the adaptive control only for fully powered underwater robot, and this controller cannot be applied to underpowered systems. In this chapter, the robust adaptive control will be designed for underdriven vertical dynamics with unknown parameters of hydrodynamic and bounded disturbances.
An underactuated system like RHUG cannot use the adaptive control to control pitch angle and speed without decoupling these dynamics.
Adaptive sliding-mode control for pitch motion
Therefore, the RHUG dynamics can only be controlled using the backward technique for pitch and speed control. Therefore, the derivative of the virtual control can be easily shown as (221) as a function of the two errors and the desired value of the angular acceleration of the height. Deriving the derivative of the Lyapunov function, the control law for 𝜏𝑞 can be established as (225) using the adaptation law, error stabilizer, perturbation estimation, and sliding mode control.
In practice, the disturbance can be modeled as the zero-mean function, such as a sinusoidal function or random function.
Adaptive sliding-mode control for surge motion
The sliding surface is chosen as 𝑠 = 𝑒2 and it is presented in the control law under the saturation function as described in fig. To derive the control law from the Lyapunov function, the derivative of 𝑉3 should be derived with the presence of the system dynamics, as shown in (232). The first term is the fitting result to estimate the unknown nonlinear part, and the second term is added to create the negative term with .
The third term is the estimated magnitude of the external disturbance (normally chosen as zero), and the final term is the robust action with the saturation function for the noiseless sliding mode control.
LOS and PI depth-keeping guidance
In case the position sensor is missing, the vehicle can use PI guidance for depth control and only depth information is required in (240), where 𝑘𝑝 is the proportional gain and 𝑘𝑖 is the integral gain. To verify the proposed control scheme, the simulation of depth control will be explained in the next subsection.
Computer simulation
- Simulation 1
- Simulation 2
Due to the slow movement of the pistons in the buoyancy motor, at the desired depth of 200m, it is. The 6th estimated parameter is for 𝑀𝑢𝑢, the 9th estimated parameter is for (−𝑧𝑔𝑊 + 𝑧𝑏𝐵), and the 10th estimated parameter is for (−𝑥𝑔𝑊 + 𝑐𝑏). The PI guidance and speed control are disabled and the desired pitch angle is set to 30° in fig.
65c the angle of inclination is the blue line and the desired angle is the red dotted line.
Conclusion
Jones, "SLOCUM: An underwater glider powered by environmental energy," IEEE Journal of oceanic engineering, vol. Miwa, "Modeling and motion simulation of an underwater glider with independently controllable main wings," in Oceans 2009-Europe, 2009, p Abdullah, "Design of an underwater hover platform for shallow water applications,” International Journal of Intelligent Defense Support Systems , vol.
Wu, “Planning and flight performance of a hybrid hydrofoil glider,” International Journal of Advanced Robotic Systems, p.
