Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

An extremum-seeking control approach for constrained robotic motion tasks

In this paper, we propose two adaptive control schemes for multiple-input systems for execution of robot end-effector movements in the presence of parametric system uncertainties. The design of these schemes is based on Model Reference Adaptive Control (MRAC) while the adaptation of the controller parameters is achieved by Extremum Seeking Control (ESC). The two control schemes, which are called Multiple-Input ESC–MRAC and Multiple-Input Adaptive-Dynamic-Inversion ESC–MRAC, are suitable for linear and nonlinear systems respectively. Lyapunov and averaging analysis shows that the proposed schemes achieve practical asymptotic reference state tracking. The proposed methods are evaluated in simulations and in a real-world robotic experiment.     

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed H₂/H_∞ approach under uncertain plant parameters and external disturbances for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X–Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H₂/H_∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The combinations of these control methods would insure the stability, robustness, optimality, overcome the uncertainties, and performance properties of the two-axis motion control system. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Moreover, the structure and the parameter learning phases of the SORFWNNC are performed concurrently and online. Furthermore, a robust controller is designed to deal with the uncertainties, including the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H₂/H_∞ controller is designed such that the quadratic cost function is minimized and the worst case effect of the unknown nonlinear dynamic function on the tracking error must be attenuated below a desired attenuation level. The mixed H₂/H_∞ control design has the advantage of both H₂ optimal control performance and H_∞ robust control performance. The sufficient conditions are developed for the adaptive mixed H₂/H_∞ tracking problem in terms of a pair of coupled algebraic equations instead of coupled nonlinear differential equations. The coupled algebraic equations can be solved analytically. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H₂/H_∞ tracking performance so that the stability of the proposed IATCS can be guaranteed. Furthermore, the control algorithms are implemented in a DSP-based control computer. From the experimental results, the motions at X-axis and Y-axis are controlled separately, and the dynamic behaviors of the proposed IATCS can achieve favorable tracking performance and are robust to parameter uncertainties.     

Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

In this paper, both output-feedback iterative learning control (ILC) and repetitive learning control (RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.      

GA-based nonlinear predictive switching control for a boiler-turbine system

In this paper,the problem of designing a controller for a highly coupled constrained nonlinear boilerturbine system is addressed with a predictive controller.First,a nonlinear predictive control is imp...     

Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes

In this paper, iterative learning control (ILC) system is modeled and designed from a two-dimensional (2D) system point of view. Based on a 2D cost function defined over a single-cycle or multi-cycle prediction horizon, two ILC schemes, referred respectively as single-cycle and multi-cycle generalized 2D predictive ILC (2D-GPILC) schemes, have been proposed and formulated in the GPC framework for the 2D system. Analysis shows that the resulted control schemes are the combination of a time-wise GPC and a cycle-wise ILC optimized in 2D sense. Guidelines for parameter tuning have been proposed based on the ultimate performance analysis for the control system. Simulation shows that the multi-cycle 2D-GPILC outperforms the single-cycle 2D-GPILC in term of cycle-wise convergence.     

混合动力电动汽车的跟车控制与能量管理

混合动力电动汽车(Hybrid electric vehicles,HEVs)的能量管理问题至关重要,而混合动力电动汽车的跟车控制不仅涉及跟车效果与安全性,也影响着能量的高效利用.将HEVs的跟车控制与能量管理相结合,提出一种基于安全距离的HEVs车辆跟踪与能量管理控制方法.首先,考虑坡度、载荷变动建立了HEVs车辆跟...     

Optimized tracking control using reinforcement learning strategy for a class of nonlinear systems

This paper is to develop a simplified optimized tracking control using reinforcement learning (RL) strategy for a class of nonlinear systems. Since the nonlinear control gain function is considered in the system modeling, it is challenging to extend the existing RL-based optimal methods to the tracking control. The main reasons are that these methods' algorithm are very complex; meanwhile, they also require to meet some strict conditions. Different with these exiting RL-based optimal methods that derive the actor and critic training laws from the square of Bellman residual error, which is a complex function consisting of multiple nonlinear terms, the proposed optimized scheme derives the two RL training laws from negative gradient of a simple positive function, so that the algorithm can be significantly simplified. Moreover, the actor and critic in RL are constructed by employing neural network (NN) to approximate the solution of Hamilton–Jacobi–Bellman (HJB) equation. Finally, the feasibility of the proposed method is demonstrated in accordance with both Lyapunov stability theory and simulation example.     

Real‐time thermal Management of Open‐Cathode PEMFC system based on maximum efficiency control strategy

Improving the efficiency of the proton exchange membrane fuel cell (PEMFC) system is the main problem should be solved for the commercialization of PEMFC. In this paper, the efficiency characteristic of PEMFC system under load and stack temperature variation is analyzed through theoretical modeling and experimental verification. For high efficiency operation of PEMFC system, the maximum efficiency control strategy (MECS) is proposed for thermal management of PEMFC system. According to the efficiency characteristics of PEMFC system, the optimal efficiency trajectory of system is obtained under maximum efficiency optimization (MEO). As PEMFC system is a nonlinear system with the characteristics of time‐variation, and uncertainty, a constrained generalized predictive control (CGPC) is proposed to realize real‐time optimal efficiency trajectory tracking. The MECS based on MEO and CGPC is implemented on‐line, experimentally validated on the established H‐300 open‐cathode PEMFC system. The experimental result shows better tracking ability compared with PID control and testifies the validity of MECS for increasing the system efficiency. Therefore, the proposed MECS can provide better system dynamic response and higher system efficiency.     

Model predictive and non-cooperative dynamic game fault recovery control strategies for a network of unmanned underwater vehicles

In this paper, the problem of control and fault recovery for a team of autonomous underwater vehicles in the presence of loss of effectiveness (LOE) actuator faults is addressed. Towards this end, two different fault recovery control strategies based on the model predictive control technique as well as the dynamic game theory are proposed and developed. Given the allowable information that can be exchanged among the agents, both centralised and semi-decentralised recovery control schemes are considered and their associated corresponding fault recovery strategies are developed. The proposed active fault recovery control strategies incorporate both the online inaccurate as well as delayed actuator fault estimates to reconfigure the nominal (healthy state) controllers. The effectiveness of the proposed semi-decentralised fault recovery control schemes is quantitatively investigated through extensive simulation case studies considering various LOE actuator fault severities in one or more unmanned vehicles as well as fault detection and isolation module imperfections such as fault estimation error and time delays in detecting the faults. The simulation results demonstrate and illustrate that our proposed semi-decentralised recovery control scheme can maintain acceptable degraded tracking and formation keeping performance of both the faulty and healthy agents in the team with lower computational and communication bandwidth requirements as well as lower or fairly close control effort cost as compared to the centralised control recovery scheme.     

Motion Control of a Nonholonomic Mobile Manipulator in Task Space

In this paper, the motion control of a mobile manipulator subjected to nonholonomic constraints is investigated. The control objective is to design a computed‐torque controller based on the coupled dynamics of the mobile manipulator. The proposed controller achieves the capability of simultaneous tracking of a reference velocity for the mobile base and a reference trajectory for the end‐effector. The aforementioned reference velocity and trajectory are defined in the task space, such task setting imitates the actual working conditions of a mobile manipulator and thus makes the control problem practical. To solve this tracking problem, a steering velocity is firstly designed based on the first‐order kinematic model of the nonholonomic mobile base via dynamic feedback linearization. The main merit of the proposed steering velocity design is that it directly utilizes the reference velocity set in the task space without requiring the knowledge of a reference orientation. A torque controller is subsequently developed based on a proposed Lyapunov function which explicitly considers the coupled dynamics of the mobile manipulator to ensure the mobile base and end‐effector track the reference velocity and trajectory respectively. This proposed computed‐torque controller is able to realize asymptotic stability of both the base velocity tracking error and the end‐effector motion tracking error. Simulations are conducted to demonstrate the effectiveness of the proposed controller.     

Adaptive dynamic surface control for linear multivariable systems

In this paper, an output-feedback adaptive control is presented for linear time-invariant multivariable plants. By using the dynamic surface control technique, it is shown that the explosion of complexity problem in multivariable backstepping design can be eliminated. The proposed scheme has the following features: (1) The L_∞ performance of the system’s tracking error can be guaranteed, (2) it has least number of updated parameters in comparison with other multivariable adaptive schemes, and (3) the adaptive law is necessary only at the first design step, which significantly reduces the design procedure. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.     

Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional–derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) – chosen as the feedforward filter – and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.     

A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites

In this paper, an active distributed (also referred to as semi-decentralised) fault recovery control scheme is proposed that employs inaccurate and unreliable fault information into a model-predictive-control-based design. The objective is to compensate for the identified actuator faults that are subject to uncertainties and detection time delays, in the attitude control subsystems of formation flying satellites. The proposed distributed fault recovery scheme is developed through a two-level hierarchical framework. In the first level, or the agent level, the fault is recovered locally to maintain as much as possible the design specifications, feasibility, and tracking performance of all the agents. In the second level, or the formation level, the recovery is carried out by enhancing the entire team performance. The fault recovery performance of our proposed distributed (semi-decentralised) scheme is compared with two other alternative schemes, namely the centralised and the decentralised fault recovery schemes. It is shown that the distributed (semi-decentralised) fault recovery scheme satisfies the recovery design specifications and also imposes lower fault compensation control effort cost and communication bandwidth requirements as compared to the centralised scheme. Our proposed distributed (semi-decentralised) scheme also outperforms the achievable performance capabilities of the decentralised scheme. Simulation results corresponding to a network of four precision formation flight satellites are also provided to demonstrate and illustrate the advantages of our proposed distributed (semi-decentralised) fault recovery strategy.     

Practical adaptive fractional‐order nonsingular terminal sliding mode control for a cable‐driven manipulator

For the high precise tracking control purpose of a cable‐driven manipulator under lumped uncertainties, a novel adaptive fractional‐order nonsingular terminal sliding mode control scheme based on time delay estimation (TDE) is proposed and investigated in this paper. The proposed control scheme mainly has three elements, ie, a TDE element applied to properly compensate the lumped unknown dynamics of the system resulting in a fascinating model‐free feature; a fractional‐order nonsingular terminal sliding mode (FONTSM) surface element used to ensure high precision in the steady phase; and a combined reaching law with adaptive technique adopted to obtain fast convergence and high precision and chatter reduction under complex lumped disturbance. Stability of the closed‐loop control system is analyzed with the Lyapunov stability theory. Comparative simulations and experiments were performed to demonstrate the effectiveness of our proposed control scheme using 2‐DOF (degree of freedom) of a cable‐driven manipulator named Polaris‐I. Corresponding results show that our proposed method can ensure faster convergence, higher precision, and better robustness against complex lumped disturbance than the existing TDE‐based FONTSM and continuous FONTSM control schemes.     

具有磁滞输入非线性系统的鲁棒自适应控制

就一类具有磁滞输入的严反馈非线性系统, 提出了一种鲁棒自适应动态面控制方案. 该方案可克服传统反推控制带来的“微分爆炸”问题, 保证闭环系统的半全局稳定性, 且跟踪误差可收敛到任意小的残集内. 特别地, 通过引入动态面修正及初始化技巧, 可保证系统跟踪误差的L∞ 性能指标. 数值仿真验证了本文所提方法案的有效性.     

Robust control of mismatched buck DC–DC converters by PWM-based sliding mode control schemes

In this paper, two control methods are proposed to control mismatched dc–dc buck converters. In the first method, called Method I, a multiple surface sliding mode control is proposed to handle mismatched load uncertainty. A major problem associated with multiple surface sliding mode control viz. ‘explosion of terms’ is handled by a disturbance observer. Another method, Method II, based on simultaneous state and disturbance observer is proposed as a further improvement over Method I in terms of sensor requirement. The practical stability of the proposed schemes is proved. The performance of both the methods are assessed for regulation and tracking of output voltage under various uncertainties and is compared with a method based on extended state observer. It is shown by simulation and experimentation that the transient and steady state performance of both the controllers are satisfactory.     

Adaptive fault-tolerant control for multiple Euler–Lagrange systems considering time delays and output constraints

Considering the existence of communication delays, the adaptive fault-tolerant control problems for constrained multiple Euler–Lagrange systems are considered in this paper. First, a communication delays observer is utilized to enable all followers can acquire the leader's state information. Second, based on neural network technique, two distributed coordinated control schemes are designed to make every follower can track the leader. Compared with the first control algorithm, the adaptive fault-tolerant control problem is further considered in the second control algorithm. The proposed algorithm can simultaneously compensate the actuator bias faults and the partial loss of actuation effectiveness faults. At the same time, a tan-type barrier Lyapunov function is used to constrain the tracking errors of all followers and the leader. Finally, the simulations show the effectiveness of the proposed control schemes.     

A fully adaptive decentralized control of robot manipulators

In this paper, we develop a fully adaptive decentralized controller of robot manipulators for trajectory tracking. With high-order and adaptive variable-structure compensations, the proposed scheme makes both position and velocity tracking errors of robot manipulators globally converge to zero asymptotically while allowing all signals in closed-loop systems to be bounded, even without any prior knowledge of robot manipulators. Thus this control scheme is claimed to be fully adaptive. Even when the proposed scheme is modified to avoid the possible chattering in actual implementations, the overall performance will remain appealing. Finally, numerical results are provided to verify the effectiveness of the proposed schemes at the end.     

具有外部扰动的PMSM系统的无模型自适应预测控制

针对于一类具有强耦合、负载不确定性和外界干扰的永磁同步电机(PMSM)系统,本文研究了基于无模型自适应预测控制(MFAPC)问题,并提出了一种新颖的MFAPC方案来实现有外界扰动的PMSM系统的速度追踪任务.所提出控制方案的主要优势在于仅使用了被控系统的输入/输出数据,并且对外界干扰具有较强的鲁棒性.此外,还讨论了闭环系统跟踪误差的收敛性以及有界输入有界输出的稳定性.最后,通过与传统的PI控制方案、原型无模型自适应控制(MFAC)方案的仿真结果相比较,验证了所提出MFAPC方案的有效性和优越性.