Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

Adaptive prescribed performance control for hydraulic system with disturbance compensation

In this article, an adaptive prescribed performance controller is developed for hydraulic system with uncertainties. An extraordinary feature is that better prescribed performance control can be achieved by compensating the uncertainties including parameter uncertainties and disturbances. For this reason, the transformation of system output error is realized by a prescribed performance function, which is employed to constrain the boundary of tracking error and convergence rate, then the tracking error of the original system with a priori prescribed performance can be realized by stabilizing the transformed system. Adaptive control is employed to solve the system parametric uncertainties; extended state observers are built to estimate the multiple disturbances. Based on the backstepping method, they are integrated into the design of the novel controller to guarantee prescribed tracking error performance. The stability analysis of the proposed controller is carried out via the Lyapunov theory. Finally, experimental results indicate good performance of the proposed algorithm.     

Adaptive motion/force tracking control of holonomic constrained mechanical systems: a unified viewpoint

This paper proposes a robust adaptive motion/force tracking controller for holonomic constrained mechanical systems with parametric uncertainties and disturbances. First, two types of well‐known holonomic systems are reformulated as a unified control model. Based on the unified control model, an adaptive scheme is then developed in the presence of pure parametric uncertainty. The proposed controller guarantees asymptotic motion and force tracking without the need of extra conditions. Next, when considering external disturbances, control gains are designed by solving a linear matrix inequality (LMI) problem to achieve prescribed robust performance criterion. Indeed, arbitrary disturbance/parametric error attenuation with respect to both motion and force errors along with control input penalty are ensured in the L₂‐gain sense. Finally, applications are carried out on a two‐link constrained robot and two planar robots transporting a common object. Numerical simulation results show the expected performances. Copyright © 2006 John Wiley & Sons, Ltd.     

Command-filtered-based neuroadaptive control for multi-input multi-output saturated nonstrict-feedback nonlinear systems with prescribed tracking performance

In this article, the prescribed performance control strategy is extended to multi-input multi-output nonstrict-feedback nonlinear systems with asymmetric input saturation, and not only each element in tracking error vector converges to a prescribed small region within preassigned finite time, but also the converging mode during the preset time is prespecifiable and controllable explicitly. By blending the barrier function with novel speed function, a prescribed performance controller using command-filtered-based vector-backstepping design framework is proposed to steer the tracking error vector for the first time, where the boundedness of filter errors is guaranteed by sufficiently small time constant and an error compensator is constructed to handle the effects of filter errors. To attenuate the adverse effects resulted from nondifferentiable input saturation, hyperbolic tangent function is utilized to estimate asymmetric saturation function such that the control input is designed as a new state variable with initial value of zero in augmented system. Nussbaum function is employed to overcome singularity problem caused by the differentiation of hyperbolic tangent function. At each step of backstepping design, the universal approximation property of neural network and the command filter system are utilized to approximate uncertain dynamics and to solve algebraic loop obstacle due to nonstrict-feedback structure, respectively. Moreover, only one parameter needs to be updated online to cope with the lumped uncertain dynamics by virtual parameter technology, rendering a control strategy with low complexity computation. The validity of the presented controller is verified by theoretical analysis and two-link robotic system.     

Non‐certainty equivalent adaptive control for lower triangular systems based on dynamic scaling and filter

A new adaptive controller is designed on the basis of dynamic scaling and filter for lower triangular systems. Compared with the available adaptive results in the literature, the proposed adaptive approach does not necessarily need to satisfy the certainty equivalence principle and allows for prescribed dynamics to be assigned to the parameter estimation error. The proposed adaptive state feedback controller that ensures all signals of closed‐loop systems are globally bounded while keeping the output tracking error to the origin simultaneously. It is interesting to note that, viewed from a Lyapunov perspective, the proposed method provides a procedure to add cross terms between the parameter estimates and the system states in every design step. Finally, two comparatively simulation examples are given, highlighting the advantages of the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.     

高超声速飞行器预设性能反演鲁棒控制

针对吸气式高超声速飞行器的飞行控制问题,提出一种新的预设性能模糊反演控制设计方法。通过构造一种新的预设性能函数,在初始误差正负未知的情况下,可以保证跟踪误差能够按照预定的收敛速度、超调量及稳态误差收敛至任意小的区域,同时实现了对跟踪误差稳态性能和瞬态性能的约束。为提高控制系统的鲁棒性,在反演控制的设计框架下,引入模糊控制器逼近动力学模型中的不确定项。为避免传统反演方法中存在的"微分膨胀"问题,引入滑模微分器对虚拟控制量的导数进行精确估计。最后,通过不同初始误差下的轨迹仿真验证所设计控制系统的有效性。     

Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state‐dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high‐frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L₂ ‐gain performance. The resulting closed‐loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive versus neural adaptive control: Application to robotics

A comparative study evaluates the problem of determining the control that must be exerted on manipulator joints. Two different techniques are studied: (i) direct and indirect adaptive controls and (ii) neural adaptive control. In the direct adaptive technique the Lyapunov stability-based approach is used with the objective of minimizing the tracking errors of the joints in the adaptation process. In the indirect adaptive technique the regulator parameters are updated via the estimation of the process model. This step, using a recursive least squares algorithm, is based on the error at the input and on the filtered dynamic model in order to avoid acceleration measurements. Neural adaptive control is based on learning from input-output measurements and not on parametricmodel-based dynamics. It is important to note that adaptive control requires a real-time estimation of the system parameters and a well-defined dynamic model, whereas neural adaptive control does not require any of these conditions. All the above-mentioned techniques are applied to the trajectory-tracking control of a two-degree-of-freedom (2DOF) manipulator. the experimental results show the effectiveness of the neural adaptive techniques for the trajectory-tracking errors.     

Adaptive prescribed performance control of nonlinear systems with unknown dead zone

An alternative adaptive control with prescribed performance is proposed to address the output tracking of nonlinear systems with a nonlinear dead zone input. An appropriate function that characterizes the convergence rate, maximum overshoot, and steady‐state error is adopted and incorporated into an output error transformation, and thus the stabilization of the transformed system is sufficient to achieve original tracking control with prescribed performance. The nonlinear dead zone is represented as a time‐varying system and Nussbaum‐type functions are utilized to deal with the unknown control gain dynamics. A novel high‐order neural network with a scalar adaptive weight is developed to approximate unknown nonlinearities, thus the computational costs can be diminished dramatically. Some restrictive assumptions on the system dynamics and the dead‐zone are circumvented. Simulations are included to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

This paper studies the connected cruise control problem for a platoon of human‐operated and autonomous vehicles. The autonomous vehicles can receive motional data, ie, headway and velocity information from other vehicles by wireless vehicle‐to‐vehicle communication. The use of wireless communications in information exchange between vehicles inevitably causes input delay in the platooning system. Meanwhile, unpredictable behaviors of the leading vehicle constitute exogenous disturbance for the system. An adaptive optimal control problem with input delay and disturbance is formulated, and a novel data‐driven control solution is proposed such that each vehicle in the platoon can achieve safe distance and desired velocity. By adopting an adaptive dynamic programming technique with sampled‐data system theory, a data‐driven adaptive optimal control approach is proposed for autonomous vehicles by the learning strategies of policy iteration without the accurate knowledge of the dynamics of all human drivers and vehicles. The efficacy of the proposed controller is substantiated by rigorous analysis and validated by simulation results in different scenarios.     

Command filter-based adaptive neural network controller design for uncertain nonsmooth nonlinear systems with output constraint

This paper investigates the command filter-based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. First, an integral barrier Lyapunov function is introduced to deal with the symmetric output constraint and make the output comply with prescribed restrictions. Second, by the Filippov's differential inclusion theory and approximation theorem, the considered nonsmooth nonlinear system is converted to an equivalent smooth nonlinear system. Third, the Levant's differentiator is used to deal with the “explosion of complexity” problem. An error compensation mechanism is established to attenuate the effect of the filtering error on control performance. Then, an adaptive neural network controller is set up by resorting to the backstepping technique. It is strictly mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin and all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, a numerical example and an application example of the robotic manipulator system are provided to demonstrate the availability of the proposed control strategy.     

Adaptive cooperative tracking control of uncertain nonlinear multiagent systems with uncertain Markov switching communication graphs

This paper deals with the cooperative tracking problem of nonlinear multiagent systems. Compared with the existing works, both the uncertainties in model and switching topology are considered. Two control laws, the adaptive distributed controller based on state information and the adaptive distributed controller based on output information, are proposed using the neural networks. The advantage of the proposed controller is that we no longer require the exact knowledge of follower agents' parameters and the precise switching signal of communication topology by taking advantages of neural networks approximation and the property of transition probabilities. It is proved that all followers can track the leader with permitted bounded errors under the proposed controller. An illustration is given to testify the efficacy of the proposed approach.     

Robust adaptive control of nonlinearly parameterized systems with unmodeled dynamics

Many physical systems such as biochemical processes and machines with friction are of nonlinearly parameterized systems with uncertainties. How to control such systems effectively is one of the most challenging problems. This paper presents a robust adaptive controller for a significant class of nonlinearly parameterized systems. The controller can be used in cases where there exist parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The design of the controller is based on the control Lyapunov function method. A dynamic signal is introduced and adaptive nonlinear damping terms are used to restrain the effects of unmodeled dynamics, nonlinear uncertainties and unknown bounded disturbances. The backstepping procedure is employed to overcome the complexity in the design. With the proposed method, the estimation of the unknown parameters of the system is not required and there is only one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. It is proved theoretically that the proposed robust adaptive control scheme guarantees the stability of nonlinearly parameterized system. Furthermore, all the states approach the equilibrium in arbitrary precision by choosing some design constants appropriately. Simulation results illustrate the effectiveness of the proposed robust adaptive controller. __________ Translated from Journal of Sichuan University (Engineering Science Edition), 2005, 37(5): 148–153 (in Chinese)     

Adaptive tracking control design of constrained robot systems

Both dynamic state feedback as well as output feedback tracking control designs are presented in this paper for constrained robot systems under parametric uncertainties and external disturbances. The previous studies on tracking control design, not considering the velocity measurements, address only the unconstrained robot design. In contrast, a dynamic output feedback controller based on a linear and reduced-order observer that uses only position measurements is proposed here for the first time to treat the trajectory tracking control problem of constrained robot systems. Both adaptive state feedback control schemes and adaptive output feedback control schemes with a guaranteed H^∞ performance are constructed. It is shown that all the variables of the closed-loop system are bounded and a pre-assigned H^∞ tracking performance is achieved, in the sense that the influence of external disturbance on the tracking motion error can be attenuated to any specified level. Moreover, it is also shown that the motion and force trajectories asymptotically converge to the desired ones as the dynamic model of robot systems is well-known and the external disturbance is neglected. Finally, simulation examples are presented to illustrate the tracking performance of a two-link robotic manipulator with a circular path constraint by the proposed control algorithms. © 1998 John Wiley & Sons, Ltd.     

Neural‐network‐based finite‐time H∞ control for extended Markov jump nonlinear systems

This paper presents a neural‐network‐based finite‐time H_∞ control design technique for a class of extended Markov jump nonlinear systems. The considered stochastic character is described by a Markov process, but with only partially known transition jump rates. The sufficient conditions for the existence of the desired controller are derived in terms of linear matrix inequalities such that the closed‐loop system trajectory stays within a prescribed bound in a fixed time interval and has a guaranteed H_∞ noise attenuation performance for all admissible uncertainties and approximation errors of the neural networks. A numerical example is used to illustrate the effectiveness of the developed theoretic results. Copyright © 2009 John Wiley & Sons, Ltd.     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

Incipient sensor fault estimation and accommodation for inverter devices in electric railway traction systems

This paper proposes an incipient sensor fault estimation and accommodation method for three‐phase PWM inverter devices in electric railway traction systems. First, the dynamics of inverters and incipient voltage sensor faults are modeled. Then, for the augmented system formed by original inverter system and incipient sensor faults, an optimal adaptive unknown input observer is proposed to estimate the inverter voltages, currents and the incipient sensor faults. The designed observer guarantees that the estimation errors converge to the minimal invariant ellipsoid. Moreover, based on the output regulator via internal model principle, the fault accommodation controller is proposed to ensure that the v_od and v_oq voltages track the desired reference voltages with the tracking error converging to the minimal invariant ellipsoid. Finally, simulations based on the traction system in CRH2 (China Railway High‐speed) are presented to verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi‐time scales

This paper deals with adaptive nonlinear identification and trajectory tracking problem via dynamic multilayer neural network with different time scales. By means of a Lyapunov‐like analysis, we determine stability conditions for the on‐line identification. Then, a sliding mode controller is designed for trajectory tracking with consideration of the modeling error and disturbance. The main contributions of the paper lie in the following aspects. First, we extend our prior identification results of single‐layer dynamic neural networks with multi‐time scales to those of multilayer case. Second, the e‐modification in standard use in adaptive control is introduced in the on‐line update laws to guarantee bounded weights and bounded identification errors. Third, the potential singularity problem in controller design is solved by using new update laws for the NN weights so that the control signal is guaranteed bounded. The stability of proposed controller is proved by using Lyapunov function. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Direct adaptive neural control of nonlinear strict‐feedback systems with unmodeled dynamics using small‐gain approach

In this paper, a novel direct adaptive neural control approach is presented for a class of single‐input and single‐output strict‐feedback nonlinear systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. Radial basis function neural networks are used to approximate the unknown and desired control signals, and a direct adaptive neural controller is constructed by combining the backstepping technique and the property of hyperbolic tangent function. It is shown that the proposed control scheme can guarantee that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main advantage of this paper is that a novel adaptive neural control scheme with only one adaptive law is developed for uncertain strict‐feedback nonlinear systems with unmodeled dynamics. Simulation results are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

Globally asymptotic adaptive controller/observer for tracking in robots without velocity measurement

In this work, we present an adaptive observer/controller for a multiple degree of freedom robotic plant without velocity measurement and without knowledge of plant parameter values. For this considered plant, we propose and present an adaptive observer/controller that estimates or observes the velocity and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally asymptotically and that all closed‐loop signals remain bounded. A contribution of the present paper, as compared with previous work for this same plant, can be deemed to be the fact that, to the best of our knowledge, the present paper is the first proven globally asymptotic result for this plant for which the size of the control torque does not increase exponentially with respect to the size of the tracking error. The control torque is discontinuous, however, only at isolated time instants. No sliding modes are used. Copyright © 2011 John Wiley & Sons, Ltd.