A direct method for multi-loop PI/PID controller design

Difficulties caused by the interactions are always encountered in the design of multi-loop control systems for MIMO processes. To overcome the difficulties, a multi-loop system is decomposed into a number of equivalent single loops for design. For each equivalent single loop, an effective open-loop process (EOP) is formulated without prior knowledge of controller dynamics in other loops, and, hence, controller can be designed directly and independently. Based on the derived EOPs, a model-based method aims at having reasonable gain margins (e.g. 2) and phase margins (e.g. ≈60°) are presented to derive multi-loop PI/PID controllers. This proposed method is formulated in details for the EOPs of 2-loop systems. Extension to higher dimensional systems needs further simplification and is illustrated with formulation for 3-loop systems. Simulation results show that this presented method is effective for square MIMO processes, especially, for low dimensional ones.     

Robust adaptive admittance control of an exoskeleton in the presence of structured and unstructured uncertainties

In this paper, an admittance control scheme for a user-in-charge exoskeleton is presented. The controller basically consists of a composite adaptive controller implementing a feedback law to estimate the structured uncertainties and to modify the apparent dynamics of the robot, and an LWPR estimator which tries to give an appropriate approximation of unmodeled uncertainty along with a robust term aiming to overcome the approximation residue. The control scheme offers a unified general control structure that explains the effect of each control component on the others. It is proved that based on the developed controller, the tracking and estimation errors converge to small boundaries with ultimate boundedness property due to the presence of the unstructured uncertainty. Based on simulations of a 2-DOF leg, the effectiveness of the controller is investigated. The results show the effectiveness of employing a universal approximator alongside a robust adaptive control and the success of the recommended approach in estimating model parameters and unmodeled dynamics simultaneously.     

DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques

This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor. The current dynamics, machine parameters and mismatched load variations are considered. The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation. Meanwhile, the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information. The effectiveness is experimentally verified using hardware comprising the QUBE-Servo2, myRIO-1900, and LabVIEW.      

Passivity-based tracking control of an omnidirectional mobile robot using only one geometrical parameter

This paper presents an output feedback tracking control scheme for a three-wheeled omnidirectional mobile robot, based on passivity property and a modified generalized proportional integral (GPI) observer. The proposed control approach is attractive from an implementation point of view, since only one robot geometrical parameter (i.e., contact radius) is required. Firstly, a nominal dynamic model is given and the passivity property is analyzed. Then the controller is designed based on passivity property and a modified GPI observer. The controller design objective is to preserve the passivity property of the robot system in the closed-loop system, which is conceptually different from the traditional model-based control methodology. Particularly, the designed control system takes full advantage of the robot natural damping. Therefore, only considerably small or non differential feedback is needed. In addition, theoretical analysis is given to show the closed-loop stability behavior. Finally, experiments are conducted to validate the effectiveness of the proposed control system design in both tracking and robustness performance.     

Bridging the gap between designed and implemented controllers via adaptive robust discrete sliding mode control

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is created due to (i) digital implementation of controller software that introduces sampling and quantization uncertainties, and (ii) uncertainties in the modeled plant's dynamics. In this paper, a new adaptive and robust model-based control approach is developed based on a nonlinear discrete sliding mode controller (DSMC) formulation to mitigate implementation imprecisions and model uncertainties, that consequently minimizes the gap between designed and implemented controllers. The new control approach incorporates the predicted values of the implementation uncertainties into the controller structure. Moreover, a generic adaptation mechanism will be derived to remove the errors in the nonlinear modeled dynamics. The proposed control approach is illustrated on a nonlinear automotive engine control problem. The designed DSMC is tested in real-time in a processor-in-the-loop (PIL) setup using an actual electronic control unit (ECU). The verification test results show that the proposed controller design, under ADC and model uncertainties, can improve the tracking performance up to 60% compared to a conventional controller design.     

Robust control and estimation of clutch-to-clutch shifts

Clutch-to-clutch shifts are ubiquitous in automatic transmissions, motivating the need for formal and robust methods for controlling these shifts. Limited sensing in production transmissions poses a severe hurdle for feedback control of these gearshifts. In the current study, nonlinear estimation methods are developed to compensate for limited sensing, and enable model-based closed loop control of the torque and inertia phases of shifts by manipulation of clutch pressures. During the torque phase, the offgoing clutch is controlled to emulate a one-way clutch, which ensures smooth coordination of the two clutches and reduced overall variation in the output shaft torque during the gearshift. During the inertia phase, the oncoming clutch is controlled to ensure smooth engagement at lock-up, resulting in reduction of shock and subsequent driveline oscillations. Controller performance is evaluated through numerical simulation of the proposed observer based controller on an experimentally validated high order model of a stepped production automatic transmission. The results show that shift control objectives were met by the proposed estimation and control strategy in the presence of appreciable model uncertainty and speed sensor noise, thus validating the robustness and practical effectiveness of the controller. Also, the proposed model-based controller was shown to be effective in controlling gearshifts at different power-levels (at different throttle openings), which establishes effectiveness of the same over a wide range of operating conditions.     

Neural network MBPC for mobile robot path tracking

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot path tracking. The method uses a non-linear model of mobile robot dynamics and thus allows an accurate prediction of the future trajectories. Constraints on the maximum attainable speeds are also considered by the algorithm. A multilayer perceptron is used to implement the MBPC. The perceptron has been trained to reproduce the MBPC bahaviour in a supervised way. Experimental results obtained when applying the neural network controller to a TRC labmate mobile platform are given in the paper.     

Semi-decentralized adaptive fuzzy control for cooperativemultirobot systems with H∞ motion/internal forcetracking performance

We present a semi-decentralized adaptive fuzzy control scheme for cooperative multirobot systems to achieve H(infinity) performance in motion and internal force tracking. First, we reformulate the overall system dynamics into a fully actuated system with constraints. To cope with both parametric and nonparametric uncertainties, the controller for each robot consists of two parts: 1) model-based adaptive controller; and 2) adaptive fuzzy logic controller (FLC). The model-based adaptive controller handles the nominal dynamics which results in both zero motion and internal force errors for a pure parametric uncertain system. The FLC part handles the unstructured dynamics and external disturbances. An H(infinity) tracking problem defined by a novel performance criterion is given and solved in the sequel. Hence, a robust controller satisfying the disturbance attenuation is derived being simple and singularity-free. Asymptotic convergence is obtained when the fuzzy approximation error is bounded with finite energy. Maintaining the same results, the proposed controller is further simplified for easier implementation. Finally, the numerical simulation results for two cooperative planar robots transporting an object illustrate the expected performance.     

Parameter identification of damping models in multibody dynamic simulation of mechanical systems

To reduce vibration and noise, a damping mechanism is often required in mechanical systems. Many types of dampers are currently used. In this paper, several typical damping models, i.e., structural damping, frictional damping, and viscoelastic damping, are illustrated, and their parameters are identified for multibody dynamic simulation. Linear damping, widely adopted for structural damping, is applied to beam deflection. Quadratic damping including air resistance is applied to plate deflection. To model stick phenomenon in mechanical dampers, a STV (stick-transition velocity) model was first introduced. To identify parameters, an optimization process is applied to the damping parameters. A new MSTV (modified stick-transition velocity) model is proposed for a friction damper. A modified Kelvin–Voight model is suggested for a rubber bushing model used in vehicle dynamics, and its parameters are identified. A modified Bouc–Wen model is also proposed; it includes the hysteretic behavior of an elastomer, and optimized results with parameter identification are compared to test results.     

Robust tracking of the heating value in an underground coal gasification process using dynamic integral sliding mode control and a gain-scheduled modified Utkin observer

In this paper, a model-based control and state reconstruction of an underground coal gasification (UCG) process is elaborated. In order to deploy model-based control strategies, a sophisticated model of the UCG process based on partial differential equations is approximated with a nonlinear control-oriented model that adequately preserves the fundamental dynamic characteristics of the process. A robust dynamic integral sliding mode control (DISMC) is designed based on the control-oriented model to track the desired heating value, which is one of the key indicators for evaluating the performance of an UCG process. Unknown states required for the model-based control are reconstructed using a gain-scheduled modified Utkin observer (GSMUO). In order to assess the robustness of the nonlinear control and estimation techniques, the water influx phenomenon is considered as an input disturbance. Moreover, the underlying UCG plant model is subjected to parametric variations as well as measurement noise. In order to guarantee the stability of the overall system, the boundedness of the internal dynamics is also proved. To make a fair comparison, the performance of the proposed controller is compared with an integral sliding mode control (ISMC) and a classical proportional-integral (PI) controller. Simulation results highlight the effectiveness of the proposed control scheme in terms of minimum control energy and improved tracking error. Moreover, the simulation study shows that the combination of DISMC and GSMUO exhibit robustness against an input disturbance, parametric uncertainties and measurement noise.     

Adaptive neural model-based decentralized predictive control

This paper considers the problem of developing an adaptive neural model-based decentralized predictive controller for general multivariable non-linear processes, where the equations governing the system are unknown. It derives a method for implementing a neural network model for unknown non-linear process dynamics for adaptive control. The performance of this controller is demonstrated and evaluated using a simulated chemical process: multivariable non-linear control of distillation column. The simulation results indicate that the proposed control strategies have good practical potential for adaptive control of multivariable non-linear processes.     

Nonlinear Bayesian state estimation: A review of recent developments

Online estimation of the internal states is a perquisite for monitoring, control, and fault diagnosis of many engineering processes. A cost effective approach to monitor these variables in real time is to employ model-based state estimation techniques. Dynamic model-based state estimation is a rich and highly active area of research and many novel approaches have emerged over the last few years. In this paper, we review various recent developments in the area of nonlinear state estimators from a Bayesian perspective. In particular, we focus on the constrained state estimation (including systems modeled using differential-algebraic equations), the handling of multi-rate and delayed measurements and recent advances in model parameter estimation. Recent advances on the stability analysis of the estimation error dynamics are also briefly discussed. The review aims to provide an integrated view of important ideas, from the authors' perspective that have driven the research in this area in recent years.     

Control system design of a 3-DOF upper limbs rehabilitation robot

This paper presents the control system design of a rehabilitation and training robot for the upper limbs. Based on a hierarchical structure, this control system allows the execution of sequence of switching control laws (position, force, impedance and force/impedance) corresponding to the required training configuration. A model-based nonlinear controller is used to impose the desired environment to the patient's arm. The knowledge of robot kinematics and dynamics is thus necessary to ensure haptic transparency and patient safety. The identification process of robot dynamics is emphasised and experimental identification results are given for the designed robot. The paper also presents a particular rehabilitation mode named Active-Assisted. Simulation results of this rehabilitation mode illustrate the potentialities of the overall control scheme, which can also be applied to other rehabilitation robots.     

直升机执行器故障的双时标容错控制系统设计

针对直升机的执行器故障,本文提出了一种基于双时标模型的自适应容错控制方法.根据直升机的不同状态变量响应时间不同的特点和时标分离理论,将直升机模型划分为快速(姿态动力学)和慢速(平移动力学)两种时标模型.反步控制方法和逆动力学控制方法分别被用于进行快慢两种模型控制器的设计,并在控制过程中采用了不同的控制周期.在双时标模型中,引入了执行器效率因子(actuator effectiveness factors,AEFs)用于表示执行器的健康情况.利用无色卡尔曼滤波(unscented Kalman filter,UKF)对AEFs进行了在线估计,估计结果用于快速和慢速模型控制器的自适应重构.仿真结果表明,该自适应容错控制方法,能够有效的消除执行器故障(包括常值和时变故障)对直升机飞行性能的影响,并取得良好的控制效果.     

Mobile robot navigation in a partially structured static environment, using neural predictive control

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a nonlinear model of mobile robot dynamics, and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A multilayer perceptron is used to implement the MBPC, allowing real-time implementation and also eliminating the need for high-level data sensor processing. The perceptron has been trained in a supervised manner to reproduce the MBPC behaviour. Experimental results obtained when applying the neural-network controller to a TRC Labmate mobile robot are given in the paper.     

Composite Nonlinear Bilateral Control for Teleoperation Systems With External Disturbances

This paper presents a new composite nonlinear bilateral control method based on the nonlinear disturbance observer (NDOB) for teleoperation systems with external disturbances. By introducing the estimations of NDOB and systems' nominal nonlinear dynamics into controller design, a NDOB based composite nonlinear bilateral controller is constructed to attenuate the influence of disturbance and uncertain nonlinearities. As compared with the existing bilateral control methods which usually achieve force haptic (i.e., contact force tracking) through a passive way, the newly proposed method has two major merits: 1) asymptotical convergence of both position and force tracking errors is guaranteed; 2) disturbance influence on force tracking error dynamics is rejected through the direct feedforward compensation of disturbance estimation. Simulations on a nonlinear teleoperation system are carried out and the results validate the effectiveness of the proposed controller.      

Modeling and Hierarchical Neuro-Fuzzy Control for Flexure-Based Micropositioning Systems

The micropositioning system using flexural bearing (e.g., for wafer steppers and coarse-fine positioning systems) is a system of infinite degrees of freedom. It is difficult to design a controller for the partial differential equation of the system directly. In this paper, a closed-form dynamics model is first developed using the assumed modes method and the least squares method. Then, a hierarchical neuro-fuzzy controller using backpropagation (BP) training algorithm is proposed for the precision control and active damping of the micropositioning system. Simulation results show that the suggested strategy can actively suppress the flexible vibration and have high positioning performance.     

A nonlinear control scheme for imprecisely known processes using the sliding mode and neural fuzzy techniques

This article considers the regulation control of nonlinear chemical processes whose dynamics are imprecisely known. A nonlinear control scheme that incorporates a sliding mode controller (SMC) and a neural fuzzy strategy is proposed to deal with this kind of processes. The sliding mode controller designed on the base of a previously known process model is implemented to keep system’s trajectory around the desired manifold. For extra and/or unknown dynamics that cannot be captured before the SMC design stage, an intelligent scheme of utilizing a neural fuzzy strategy is then used to provide an adaptive ability to accommodate the perturbation, which therefore is able to force the system output back to and maintain in the desired set point. The effectiveness and applicability of the proposed scheme are demonstrated through the control of a continuous stirred tank reactor with existing simultaneously the unmodeled side reactions, measuring error, and extra matched and unmatched disturbances. The potential use of a sliding observer along with the proposed scheme is also investigated in the work. Extensive simulation results reveal that the incorporation of the model-based SMC and the intelligent neural fuzzy technique appears to be an effective and promising approach to the nonlinear control of chemical processes whose dynamics are imprecisely known.     

Reinforcement learning-based output feedback control of nonlinear systems with input constraints.

A novel neural network (NN)-based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input and multi-output (MIMO) strict feedback nonlinear discrete-time systems. Reinforcement learning is proposed for the output feedback controller, which uses three NNs: 1) an NN observer to estimate the system states with the input-output data, 2) a critic NN to approximate certain strategic utility function, and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the state estimation errors, the tracking errors and weight estimates is shown.     

Flexible-link robot arm control by a feedback linearization/singular perturbation approach

A nonlinear tracking controller for the link-tip positions and velocities of a multi-link flexible robot arm is designed that gives guaranteed performance. The controller has three parts: a model-based trajectory generator, an inner loop based on input-output feedback linearization, and an outer loop that stabilizes the internal dynamics (e.g., the flexible modes) using a singular perturbation design. We show how to stabilize the internal dynamics by selecting a physically meaningful modified performance output for tracking; this output is the slow portion of the link-tip motions. That is, the tracking requirement is relaxed so that the internal dynamics are stabilizable through a boundary layer correction that attenuates the flexible mode vibrations. © 1994 John Wiley & Sons, Inc.