Robust optimal attitude control of hexarotor robotic vehicles

Multirotor aerial robotic vehicles attract much attention due to their increased load capacity and high maneuverability. In this paper, a robust optimal attitude controller is proposed for a kind of multirotor helicopters—hexarotors. It consists of a nominal optimal controller and a robust compensator. The nominal controller is designed based on the linear quadratic regulation (LQR) method to achieve desired tracking of the nominal system, and the robust compensator is added to restrain the influence of uncertainties. The key contributions of this work are twofold: firstly, the closed-loop control system is robust against coupling and nonlinear dynamics, parametric uncertainties, and external disturbances; secondly, a decoupled and linear time-invariant control architecture making it ideal for real-time implementation. The attitude tracking errors are proven to be ultimately bounded with specified boundaries. Simulation and experimental results on the hexarotor demonstrate the effectiveness of the proposed attitude control method.     

Robust nonlinear control for a model-scaled helicopter with parameter uncertainties

A robust nonlinear controller is designed for the trajectory tracking of a model-scaled helicopter with uncertain parameters (including uncertain inertial parameters, uncertain structural parameters and uncertain aerodynamic parameters). The proposed controller is based upon the backstepping technique, with modifications to accommodate helicopter dynamics. Aerodynamic uncertainties are addressed by dynamic inverters, and derivatives of virtual controls in the backstepping process are calculated through command filters to avoid complex analytical solutions. Errors resulted from dynamic inverters, command filters, and uncertain parameters are then regarded as disturbances, and treated by nonlinear damping terms. With the proposed controller, it can be proved that tracking errors of the closed-loop system are ultimately bounded with tunable ultimate bounds. Performances of the proposed controller are evaluated by simulation results.     

AIRSHIP ATTITUDE TRACKING SYSTEM

The attitude tracking control problem for an airship with parameter uncertainties and external disturbances was considered in this paper. The mathematical model of the airship attitude is a multi-input/multi-output uncertain nonlinear system. Based on the characteristics of this system, a design method of robust output tracking controllers was adopted based on the upper-bounds of the uncertainties. Using the input/output feedback linearization approach and Liapunov method, a control law was designed, which guarantees that the system output exponentially tracks the given desired output. The controller is easy to compute and complement. Simulation results show that, in the closed-loop system, precise attitude control is accomplished in spite of the uncertainties and external disturbances in the system.     

System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Trajectory tracking of a mobile manipulator is a challenging research because of its complex nonlinearity and dynamics. This paper presents an adaptive control strategy for trajectory tracking of a mobile manipulator system that consists of a wheeled platform and a modular manipulator. When a robot system moves in the presence of sliding, it is difficult to accurately track its trajectory by applying the backstepping approach, even if we employ a non-ideal kinematic model. To address this problem, we propose using a combination of adaptive fuzzy control and backstepping approach based on a dynamic model. The proposed control scheme considers the dynamic interaction between the platform and manipulator. To accurately track the trajectory, we propose a fuzzy compensator in order to compensate for modeling uncertainties such as friction and external disturbances. Moreover, to reduce approximation errors and ensure system stability, we include a robust term to the adaptive control law. Simulation results obtained by comparing several cases reveal the presence of the dynamic interaction and confirm the robustness of the designed controller. Finally, we demonstrate the effectiveness and merits of the proposed control strategy to counteract the modeling uncertainties and accurately track the trajectory.     

Neural-network-based adaptive robust precision motion control of linear motors with asymptotic tracking performance

High precision motion control of permanent magnet linear motors (PMLMs) is limited by undesired nonlinear dynamics, parameter variations, and unstructured uncertainties. To tackle these problems, this paper presents a neural-network-based adaptive robust precision motion control scheme for PMLMs. The presented controller contains a robust feedback controller and an adaptive compensator. The robust controller is designed based on the robust integral of the sign of the error method, and the adaptive compensator consists of a neural network component and a parametric component. Moreover, a composite learning law is designed for the parameter adaption in the compensator to further enhance the control performance. Rigorous stability analysis is provided by using the Lyapunov theory, and asymptotic tracking is theoretically achieved. The effectiveness of the proposed method is verified by comparative simulations and experiments on a PMLM-driven motion stage.

     

考虑摩擦特性的柔性关节空间机器人自适应CMAC神经网络鲁棒控制

讨论了关节摩擦力矩影响下,具有柔性铰关节的漂浮基空间机器人系统的动力学控制问题.设计了基于高斯基函数的小脑神经网络(CMAC)鲁棒控制器和摩擦力矩补偿器.用奇异摄动理论对系统的动力学模型进行快慢变子系统分解,针对快变子系统,设计力矩微分反馈控制器来抑制机械臂关节柔性引起的振动;对于慢变子系统,设计了基于自适应CMAC神...     

Orientation-error observer-based tracking control of nonholonomic mobile robots

In this paper, a novel trajectory tracking controller is proposed for mobile robots with unknown orientation angle by employing the orientation-error observer (OEO). In order to overcome the local stability resulted from linearization design methods, an asymptotically stable controller is designed using Lyapunov’s direct method. This method breaks down nonlinear systems into low-dimensional systems and simplifies the controller design using virtual auxiliary error function and partial Lyapunov functions. A state-feedback controller for the nonlinear error dynamics of the mobile robot is combined with an observer that estimates the orientation-error based on available trajectory information and measurement of the position coordinates. The stability of the system is easily proved via the Lyapunov theory. Abundant simulation and experiment results validate the effectiveness and superiority of the proposed control method.     

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.     

Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters

This paper presents a novel adaptive nonlinear model predictive control design for trajectory tracking of flexible-link manipulators consisting of feedback lineariza-tion, linear model predictive control, and unscented Kalman filtering. Reducing the nonlinear system to a linear system by feedback linearization simplifies the optimization prob-lem of the model predictive controller significantly, which, however, is no longer linear in the presence of parame-ter uncertainties and can potentially lead to an undesired dynamical behaviour. An unscented Kalman filter is used to approximate the dynamics of the prediction model by an online parameter estimation, which leads to an adaptation of the optimization problem in each time step and thus to a better prediction and an improved input action. Finally, a detailed fuzzy-arithmetic analysis is performed in order to quantify the effect of the uncertainties on the control structure and to derive robustness assessments. The control structure is applied to a serial manipulator with two flexible links containing uncertain model parameters and acting in three-dimensional space.     

Robust tracking control method based on composite nonlinear feedback technique for linear systems with time-varying uncertain parameters and disturbances

In this paper, the composite nonlinear feedback control method is considered for robust tracking and model following of uncertain linear systems. The control law guarantees that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of the dynamical system, the proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to allow the closed loop system have a small damping ratio and a quick response while the nonlinear feedback law increases the damping ratio of the system as the system output approaches the output of the reference model. A new collection of different nonlinear functions used in the control law are offered to improve the reference tracking performance of the system. The proposed robust tracking controller improves the transient performance and steady state accuracy simultaneously. Finally, the simulations are provided to verify the theoretical results.     

Nonlinear adaptive neural network control for a model-scaled unmanned helicopter

In this paper, a nonlinear adaptive neural network control is proposed for trajectory tracking of a model-scaled helicopter. The purpose of this research is to reduce the ultimate bounds of tracking errors resulted from small coupling forces (or small parasitic body forces) and aerodynamic uncertainties. The proposed control is designed under backstepping framework, with neural network compensators being added. Updating laws of neural networks are designed through projection algorithm, so that adaptive parameters are bounded. Derivatives of virtual controls are obtained through command filters. It is proved that, by using neural network compensators, tracking errors of the closed-loop system can be restricted within very small ultimate bounds. Superiority of the proposed nonlinear adaptive neural network control over a backstepping control is demonstrated by simulation results.     

Longitudinal–lateral velocity control design and implementation for a model-scaled unmanned helicopter

In this paper, a nonlinear controller is designed and implemented for longitudinal–lateral motion of a model-scaled helicopter. The underlying principle of controller design is the backstepping technique with slight modifications to accommodate the helicopter model. It is proved theoretically that, under the proposed controller, velocities and yaw angle of the closed-loop system are capable of tracking reference signals. A practical helicopter testbed is constructed to test the performances of the closed-loop system. Experimental results of practical flight tests demonstrate that performances of the closed-loop system are satisfactory.     

Adaptive exponential-reaching sliding-mode control for antilock braking systems

Most commercial antilock braking system (ABS) is based on a look-up table. The table is calibrated through laboratory experiments and engineering field tests under specified road conditions, but it is not adaptive. To attack this problem, this paper proposes an adaptive exponential-reaching sliding-mode control (AERSMC) system for an ABS. The proposed AERSMC system is composed of an equivalent controller and an exponential compensator. The equivalent controller uses a functional-linked wavelet neural network (FWNN) to online approximate the system uncertainties and the exponential compensator is designed to eliminate the effect of the approximation error introduced by the FWNN uncertain observer with an exponential-reaching law. In addition, the adaptive laws online-tune the controller parameters in the sense of Lyapunov function to guarantee the system stability. Finally, the simulation results verify that the proposed AERSMC system can achieve favorable slip tracking performance and is robust against parameter variations in the plant.     

Robust Tracking of a Lightweight Manipulator System

A robust control design for high performance joint trajectory tracking of a flexible lightweight manipulator system is proposed. The design is based on a combined controller-observer scheme involving the sliding manifold approach and the optimal interpolation technique. This controller provides the designer with an enhanced joint tracking performance when the system is subject to parametric variations due to structural disturbances caused by link flexibility and load uncertainties. The parametric variations are handled by sliding control and the estimation of the nonlinearly excited elastic dynamics by an optimal interpolator of the structure's dynamic responses. The design procedure is progressive, i.e., we start with a basic controller and then modify it in order to improve the performance. Closed loop simulations with the various designed controllers are used to validate the analytical results and to help choosing the most suitable one.     

Robust dynamic surface control of nonlinear systems with prescribed performance

In this paper, a robust dynamic surface controller with prescribed performance for a class of nonlinear feedback systems is proposed. Utilizing the prescribed performance control (PPC), the prescribed steady state and transient performance for the tracking error of the original system can be ensured through the stabilization of a transformed system. The dynamic surface control procedure solves the mismatched uncertainties and the explosion of the complexity problem. The uncertainties can be eliminated by the constructed compensation signals of a low-pass filter. And it is proven in the performance analysis that the proposed controller is of low complexity and has improved system robustness. Simulation results verify the proposed approach.     

A self-tuning guidance and control system for marine surface vessels

An integrated guidance and control system has been developed to enable underactuated marine surface vessels to operate autonomously and yield robust tracking performance in spite of significant external disturbances and modeling imprecision. A nonlinear ship model, accounting for all six degrees-of-freedom of the ship, has been used as a test bed to assess the performance of the proposed scheme. The controller combines the advantages of the variable structure systems (VSS) theory with the self-tuning fuzzy logic scheme. It does not require an accurate dynamic model of the ship or the construction of a rule-based expert system. Its asymptotic stability is ensured by knowing the upper bounds on modeling imprecision and external disturbances and by forcing the tuning parameters to satisfy the sliding conditions. The guidance system is based on the concepts of the variable radius line-of-sight (LOS) and the acceptance circle around the waypoints. The current system varies the LOS radius exponentially with the cross track error in order to achieve a fast convergence rate of the ship to its desired trajectory. The simulation results demonstrate the robust tracking characteristic of the integrated guidance and control system in spite of significant modeling uncertainties and environmental disturbances.     

Neural network based robust hybrid control for robotic system: an H �� approach

A novel robust hybrid tracking control for robotic system is proposed. This hybrid control scheme combines computed torque control (CTC) with neural network, variable structure control (VSC) and nonlinear H _?? control methods. It is assumed that the nominal system of robotic system is completely known, which is controlled by using CTC method. Neural network is designed to approximate parameter uncertainties, VSC is used to eliminate the effect of approximation error, and H _?? control is employed to achieve a desired robust tracking performance. Based on Lyapunov stability theorem, it can be guaranteed that all signals in closed loop are bounded and a specified H _?? tracking performance is achieved by employing the proposed robust hybrid control. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

单足机器人周期跳跃控制的虚拟约束方法

 研究单足机器人周期跳跃控制问题．弹簧支撑倒立摆模型可以比较准确地描述动物的跳跃行为,但无控制的自然跳跃抗干扰能力较差,一般采用轨迹跟踪控制方法实现单足机器人周期跳跃．当系统存在比较大的误差时,传统的时间轨迹跟踪控制方法存在明显的不足．引入虚拟约束技术,采用基于空间路径跟踪的控制方法可以克服时间轨迹跟踪的不足．采用点足机器人模型,并通过控制腿伸缩的方式为系统提供动力,将跳跃过程分为地面摆动和腾空飞行两个阶段,并通过起飞和着陆两个事件完成两个阶段之间的转换,整个系统模型属于欠驱动非光滑动力学系统．根据简化的动力学方程获得系统的虚拟约束解析表达式,并采用部分反馈线性化方法结合PD 控制设计系统的控制律．分析了系统的混合零动力学方程,并证明了闭环系统的临界稳定性．仿真结果表明,提出的控制方法可以实现单足机器人的周期跳跃控制,并且对外部干扰具有较强的鲁棒性．     

Decentralized adaptive neural network control for mechanical systems with dead-zone input

We propose a decentralized adaptive robust controller for trajectory tracking of mechanical systems with dead-zone input in this paper. The considered mechanical systems are with high-order interconnections and unknown non-symmetric nonlinear input. In each local controller, the neural network control is introduced to estimate the uncertainties and disturbances, meanwhile the siding mode control and adaptive technical are designed to compensate for the approximation errors. A nonlinear function is chosen to deal with the interconnections. Following, the stability and robustness are verified by using Lyapunov stability theorem. Finally, simulations are provided to support the theoretical results     

惯性平台稳定回路基于Backstepping的动态滑模控制(英文)

针对带不匹配不确定非线性干扰的惯性平台稳定回路跟踪控制问题,提出了基于backstepping的动态滑模控制方法。首先,建立了惯性平台稳定回路的等价模型,该模型由一个线性模型加上一个不确定的非线性函数组成。然后,基于backstepping方法设计了带渐近稳定滑模面的动态滑模控制器,解决了模型不匹配的问题,并提高了系统的鲁棒性。进而应用Lyapunov稳定性理论证明了所设计的控制器不仅能保证闭环系统的稳定性,而且可以通过选择适当的控制器参数来调整跟踪误差的收敛率。最后,仿真结果表明,基于backstepping的动态滑模控制方法与PID控制方法相比,提高了系统的跟踪精度,增强了鲁棒性。