Robust Diving and Composite Path Tracking Control of an Autonomous Underwater Vehicle

Autonomous Underwater Vehicles (AUVs) are widely used for marine survey, in both the coastal and deep sea areas and they are applicable to both civil and defense applications. They are pre-programmed and can operate without human intervention and this makes them attractive to many marine industries. A concern with AUVs is the high energy consumption required by their thrusters for depth control, buoyancy change and manoeuvrability and that adversely affects their performance and endurance. This paper presents the design and development of novel stand-alone variable buoyancy system for AUVs and investigates its performance through numerical and experimental investigations. The design idea is based upon the Pump Driven Variable Buoyancy System (PDVBS) and uses a hydraulic based method to control the buoyancy. The VBS is integrated into a medium sized AUV of 3 m length and the performance of the vehicle in vertical plane is investigated. The results are presented for a buoyancy change requirement of 5 kg and a diaphragm type positive displacement pump, with a buoyancy change rate of 5 kg/min, is utilized. Depth control performance of the AUV and its hovering capabilities, at a desired depth of 60 m using the Linear Quadratic Regulator (LQR) controller, are analysed in detail. Finally, the results indicate that the designed and developed VBS is effective in changing the buoyancy and controlling the heave velocity. These two features are expected to provide higher endurance and better performance in AUVs involved in rescue/attack operations.

Autonomous Underwater Vehicles have gained popularity for the last decades, especially a lot of AUVs were considered as the most suitable tool for the purpose of reducing risks of people in dangerous marine operations. This paper presents the preliminary results of the research on hardware design, the controller of an autonomous underwater vehicle model for the task of survey, search and rescue ... With a compact design, AUV can operate in limited spaces. Through a unique ducted propeller and rudder located at the aft, the AUV can perform horizontal motion. It can also control pitch angle and depth motion by an inside mass shifter mechanism (MSM) which changes the vehicle center of gravity. In addition, The AUV is integrated with powerful eletronic system, highprecision sensors helping it carries on missions from simple to complex. The use of Sliding Mode Control (SMC) to independently design the heading and depth controllers for AUV demonstrates the steady stability of the controllers with the nonlinear model, uncertainty parameters and disturbances. Finally, the simulation results show that the SMC controllers can control the AUV nonlinear model to track the desired steering angle and depth with high accuracy and stability.

In this paper, a recently proposed control design technique, model reference robust control (MRRC), is generalized to provide input–output robust control for a much wider class of SISO systems. A system under consideration may contain not only parametric uncertainties, disturbances, uncertain high-order nonlinearities associated with system output (including stable unmodelled dynamics of the output), but also multiplicative unmodelled dynamics of the input of the system. Input unmodelled dynamics may be unstable and of infinite dimension. Common assumptions such as the order of the system, or bounds on system parameters, or coefficients in size-bounding functions on uncertainties and unmodelled dynamics are not needed in this paper. The a priori information required to apply the proposed design procedure includes the relative degree of the overall system, closed-form expressions of bounding functions, and, for arbitrarily small error in tracking the output of any given reference model, the minimum phase condition. Under the proposed control, the output tracking error can be made arbitrarily small to achieve any specified tracking accuracy. The distinct feature of MRRC is that neither uncertainties nor unmodelled dynamics are required to be small. © 1997 by John Wiley & Sons, Ltd.

A small-gain approach to inverse optimal adaptive control of nonlinear systems with unmodeled dynamics

SummaryIn this paper, we propose an adaptive output‐feedback controller for uncertain linear systems without a priori knowledge of the plant high‐frequency‐gain sign. To deal with parametric uncertainties and unmodeled dynamics, we consider a robust adaptive strategy named binary model reference adaptive control, which has the good transient properties and robustness of sliding mode control with the important advantage of having a continuous control signal free of chattering. The effective way of tackling unknown high‐frequency‐gain sign is employing monitoring functions. The developed adaptive control guarantees global exponential stability of the closed‐loop error system with respect to a compact residual set. Moreover, in the absence of unmodeled dynamics, exact tracking of a reference signal can be achieved. Numerical simulations illustrate the efficacy of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.

Microgrid uses renewable energy sources to generate power and this is one of the most important reasons for drawing attention of researchers. Microgrid is a small scaled power generation unit which is a set of loads, distributed generation units, voltage source inverters, PCC, filters etc. Parameters and loads are unknown and uncertain which cause unmodeled dynamics of loads. Tracking performance of voltage degrades due to different uncertainties and unmodeled load dynamics of microgrid. This paper presents the design of a data driven model predictive controller to control the voltage of a single phase islanded microgrid. The way in which the controller is designed ensures robustness, guarantees performance and stability against unmodeled dynamics, unknown loads and all other uncertainties. MATLAB/SIMULINK is used to carry out the simulation of controller and the results are found for open and closed loop system. Under different load condition and uncertainties the performance of controller is studied. Results presented in this paper indicate stability, robustness, improved voltage quality and tracking performance under mentioned uncertain and unknown conditions.

Some robust control methods have been developed in the past in order to increase tracking performance in the presence of parametric uncertainties. In the presence of parametric uncertainty, unmodelled dynamics and other sources of uncertainties, robust control laws are used. Corless-Leitmann [1] approach is a popular approach used for designing robust controllers for robot manipulators. In early application of Corless-Leitmann [1] approach to robot manipulators [2, 3], it is difficult to compute uncertainty bound precisely. Because, uncertainty bound on parameters depends on the inertia parameters, the reference trajectory and manipulator state vector. Spong [4] proposed a new robust controller for robot manipulators using the Lyapunov theory that guaranties stability of uncertain systems. In this approach, Leithmann [5] or Corless-Leithman [1] approach is used for designing the robust controller. One of the advantage of Spong’s approach [4] is that uncertainty on parameter is needed to derive robust controller and uncertainty bound parameters depends only on the inertia parameters of the robots. Yaz [6] proposed a robust control law based on Spong’s study [4] and global exponential stability of uncertain system is guaranteed. However, disturbance and unmodelled dynamics are not considered in algorithm of [4, 6]. Danesh at al [7] develop Spong’s approach [4] in such a manner that control scheme is made robust not only to uncertain inertia parameters but also to robust unmodelled dynamics and disturbances. Koo and Kim [8] introduce adaptive scheme of uncertainty bound on parameters for robust control of robot manipulators. In [8], upper uncertainty bound is not known as would be in robust controller [4] and uncertainty bound is estimated with estimation law in order to control the uncertain system. A new robust control approach is proposed by Liu and Goldenerg [9] for robot manipulators based on a decomposition of model uncertainty. Parameterized uncertainty is distinguished from unparameterized uncertainty and a compensator is designed for parameterized and unparameterized uncertainty. A decomposition-based control design framework for mechanical systems with model uncertainties is proposed by Liu [10]. In order to increases tracking performance of uncertain systems, design of uncertainty bound estimation functions are considered. For this purpose, some uncertainty bound estimation functions are developed [11-15] based on a Lyapunov function, thus, stability of

Structured and unstructured uncertainties always exist in physical servo systems and degrade their tracking accuracy. In this paper, a practical method named adaptive robust control with extended state observer (ESO) is synthesized for high-accuracy motion control of a dc motor. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties) but also the unstructured uncertainties (i.e., nonlinear friction, external disturbances, and/or unmodeled dynamics). Adaptive control for the structured uncertainty and ESO for the unstructured uncertainty are designed for compensating them respectively and integrated together via a feedforward cancellation technique. The global robustness of the controller is guaranteed by a feedback robust law. Furthermore, the controller theoretically guarantees a prescribed tracking performance in the presence of various uncertainties, which is very important for high-accuracy control of motion systems. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy.

This study investigates the use of H2, μ-synthesis, and mixed H2/μ methods to construct full-order controllers and optimized controllers of fixed dimensions. The benchmark problem definition is first extended to include uncertainty within the controller bandwidth in the form of parametric uncertainty representative of uncertainty in the natural frequencies of the design model. The sensitivity of H2 design to unmodelled dynamics and parametric uncertainty is evaluated for a range of controller levels of authority. Next, μ-synthesis methods are applied to design full-order compensators that are robust to both unmodelled dynamics and to parametric uncertainty. Finally, a set of mixed H2/μ compensators are designed which are optimized for a fixed compensator dimension. These mixed norm designs recover the H2 design performance levels while providing the same levels of robust stability as the μ designs. It is shown that designing with the mixed norm approach permits higher levels of controller authority for which the H2 designs are destabilizing. The benchmark problem is that of an active tendon system. The controller designs are all based on the use of acceleration feedback. This paper was produced under the auspices of the U.S. Government and it is therefore not subject to copyright in the U.S.

This paper presents the design of a robust direct model reference adaptive control (DMRAC) algorithm with focus on the satisfaction of an almost strictly positive real (ASPR) condition in the presence of plant uncertainty represented as either variations in plant parameters or unmodelled dynamics in the form of additive or multiplicative norm bounded perturbations in the transfer function. Both the time and frequency domain design conditions for a feedforward compensator have been developed utilizing the robust stability analysis and optimization methods. Illustrative examples are presented to validate the algorithm's use and applicability to control systems with parametric uncertainty or unmodelled dynamics.

This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

A combination BTT control mode with the single moving mass and differential aileron is proposed in this paper for some engineering problems of long-range reentry maneuverable vehicle controlled by only one actuator. This control mode can not only avoid the elevator and rudder ablation to improve the efficiency of attitude control, but also reduce the quantity of moving mass actuator. According to the dynamics model of the proposed control mode, the extended state observer is used to estimate the states of system, the external disturbances and parameter uncertainties disturbance based on an integrated manner, then the roll channel control system is designed by using LQR controller. Lastly, the simulation results are presented to demonstrate the efficacy of the extended state observer in estimation of the uncertainties and states, and the proposed design is robust to the disturbances caused by the moving mass lateral movement and the asymmetric ablation of vehicle, parameter uncertainties, unmodeled dynamics.

Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the existing literature by proposing a dedicated control framework capable of simultaneously and effectively handling parameter uncertainty, unmodeled dynamics, and external disturbances, while ensuring constraint satisfaction. Firstly, a dynamic model of a three-degree-of-freedom robotic arm was established based on the Lagrange equation; secondly, this paper designs a deep integration mechanism of adaptive law and robust predictive control: by designing a parameter adaptive algorithm to estimate the system uncertainty online and feedforward compensate it to the predictive model, the impact of model mismatch is significantly reduced; meanwhile, for the estimated residuals and unknown disturbances, feedback gain was introduced and the control input was designed based on the robust invariant set theory, achieving unified parameter identification, disturbance suppression and rolling optimization within a single framework. This paper strictly proves the feasibility and stability of the control scheme. Finally, the simulation experiments based on MATLAB show that, compared with the traditional MPC and PID methods, the APRMPC algorithm can achieve higher accuracy and stronger robustness in trajectory tracking under various working conditions, effectively resolving the inherent contradiction between the weak robustness of the traditional MPC and the large buffering of sliding mode control, and verifying the value of the proposed scheme in filling the gap in related literature.

This paper presents a detailed study of the dynamic modeling and parametric uncertainty analysis of a 3D crane system, providing insights into its positional and angular responses across varying operational conditions. Using real-world experimental data, the study identifies and quantifies key parameters, including system gains, time constants, and damping ratios. These parameters form the basis of a validated mathematical model that accurately reflects the crane's dynamic behaviour under diverse Z-axis configurations and input conditions. The results demonstrate how payload height significantly affects both positional and angular dynamics, particularly revealing notable coupling effects between translational and rotational movements. Additionally, an asymmetric actuator dead-zone was experimentally observed and analysed to enhance the comprehensiveness of the system identification. A nonlinear state-space model and the manufacturer's nominal linear translational model are used for comparison with the identified linear model. To the best of the authors' knowledge, this is the first study to experimentally derive and quantify parametric uncertainties in a validated 3D crane system model, extending beyond existing literature, which often assumes fixed parameters or focuses on lower-dimensional models. The insights from this study establish a foundation for designing robust control strategies capable of handling the inherent variability and complexity of 3D crane systems. The findings have practical implications for industries requiring precise load handling and reliable operation under dynamic and uncertain conditions, such as construction, logistics, and automated systems.

This work proposes an extension of MP-ADRC for non-minimum phase systems, applying it to the control of an Acrobot, which approximates a bicycle robot system. The objective is to take advantage of the robustness properties of MP-ADRC with respect to external disturbances, parametric uncertainties and unmodeled dynamics. A cascade control strategy is used, representing the Acrobot dynamic model as two systems in series, one of the minimum phase and the other of the non-minimum phase. An MP-ADRC is designed for each of the systems, aiming to obtain a stable closed-loop structure. Simulation results are presented with the aim of showing the effectiveness of the proposed control strategy.