A new design method for PI–PD control of unstable processes with dead time

PID controllers are still widely practiced in the industrial systems. In the literature, many publications can be found considering PID controller design for unstable processes. However, owing to the structural limitations of PID controllers, generally, good closed loop performance cannot be achieved with a PID for controlling unstable processes and usually a step response with a high overshoot and oscillation is obtained. On the other hand, PI–PD controllers are proved to give very satisfactory closed loop performances for unstable processes. The paper presents a simple design method to tune parameters of a PI–PD controller for the control of the unstable processes with time delay. The proposed method is based on plotting the stability boundary locus, which is a locus dependent on the parameters of the controller and frequency, in the parameter plane. The method uses a new concept named centroid of the convex stability region. Simulation examples and an experimental application are given to illustrate the superiority of the proposed method over some existing ones.     

An adaptive inverse control method based on SVM–fuzzy rules acquisition system for pulsed GTAW process

This paper proposes a new method of adaptive inverse control based on support vector machine–fuzzy rules acquisition system (SVM-FRAS) for the gas tungsten arc welding (GTAW) process. In this control mechanism, an identifier is established based on SVM-FRAS, and an inverse controller based on SVM-FRAS is designed. The proposed adaptive inverse control method can automatically extract control rules from the process data. Comprehensibility is one of the required characteristics for a complex GTAW process control. We use the proposed SVM-FRAS-based adaptive inverse control method to obtain the rule-based process and the control model of the aluminum alloy pulse GTAW process. Based on the simulation experiments for GTAW process, the SVM-FRAS adaptive inverse control method is found to be effective.     

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.     

Initialization method for fuzzy Takagi–Sugeno systems

This paper presents a method for initializing Takagi–Sugeno fuzzy systems in which the initial values of fuzzy antecedents are obtained by dynamic decomposition of the input space while the consequent values are obtained by the recursive least squares method. The results of experiments on 13 datasets from the KEEL repository are described. The results of approximating these datasets by the proposed method are compared with those obtained by five well-known identification algorithms..     

One-step receding horizon H(∞) control for networked control systems with random delay and packet disordering

The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.     

Analysis based on microcontact mechanism for the roughness dependent stick–slip motion

The stick–slip behavior occurring between an indenter with its round tip treated as a rigid sphere with a radius of 300 nm and the (1 0 0) Si specimen was considered. The characteristic slip distances were observed in the stick–slip behavior under small normal loads. A statistical evaluation of microcontact on the basis of Fourier cosine series was proposed to evaluate the dominant scales of roughness under different normal loads. The experimental results showed the elastic deformation exhibited in the contact between the indenter and the specimen when the contact load was smaller than 40 μN. The characteristic scales of roughness obtained by the proposed method revealed good agreement with the experimental results of slip distance under different contact loads. Through the proposed method, the roughness dependence of the stick–slip behavior can be further understood quantitatively.     

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.     

A “gentle” method for finishing Si3N4 balls for hybrid bearing applications

This paper is an overview of the work carried out in the finishing of Si₃N₄ balls for bearing applications using a novel process known as the magnetic float polishing (MFP). It is a “gentle” process in which very low levels of controlled force (∼ 0.5–2 N) is applied. The process can be divided into the following three stages: (1) an initial high material removal stage where the objective is to remove material as rapidly as possible with minimal damage to the surface, (2) an intermediate stage of semifinishing where size and sphericity are carefully monitored, and (3) final finishing stage where size, sphericity, and finish are closely controlled to meet the final requirements. The first process predominantly involves mechanical removal of material with harder and coarser abrasives, e.g., coarse B₄C; the second process involves the use of “less” harder and finer abrasives, e.g., medium to fine B₄C or SiC; and the third process involves the use of “much” softer abrasives, e.g., CeO₂, ZrO₂, and Cr₂O₃ that can chemo-mechanically react with the workmaterial (Si₃N₄) and/or the environment (air or water) to generate a smooth surface. Chemo-mechanical polishing of Si₃N₄ balls with CeO₂ abrasive yields an extremely smooth and damage-free surface with a surface finish R _aof 4 nm and an R _t of 40 nm and a sphericity of 0.15–0.25 μm. This revised version was published online in July 2006 with corrections to the Cover Date.     

A self-heating 2ω method for Seebeck coefficient measurement of thermoelectric materials

A novel and reliable self-heating 2ω method has been developed to measure the Seebeck coefficient of the microscale/nanoscale thermoelectric materials. Based on the analytical solution of the transient heat-conduction equation of the specimen heated by a harmonic current, two measurement modes have been developed: (1) the Seebeck coefficient can be directly extracted from the ratio of experimentally measured 2ω Seebeck voltage to theoretically predicted 2ω temperature drop oscillation; and (2) the Seebeck coefficient can be steadily extracted from the measured 2ω and 3ω voltages. This approach has been applied to a 25.4 μm thick K-type thermocouple and the measured Seebeck coefficient corresponds well with the nominal value.     

Accurate measurement method for tube’s endpoints based on machine vision

Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube’s endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube’s endpoints based on machine vision is proposed. First, reflected light on tube’s surface can be removed by using photometric linearization. Then, based on the optimization model for the tube’s endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube’s endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint’s positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube’s endpoints without any surface treatment or any tools and can realize on line measurement.     

A novel individual pitch control algorithm based on μ-synthesis for wind turbines

In this paper, we present a novel individual pitch control algorithm based on μ-synthesis for blade load reduction for wind turbines. Our algorithm is different from the conventional individual pitch control in the sense that the multi-blade coordinate transformation is not used at all. Instead, the wind turbine model coefficients that depend on the azimuth angle are regarded as structured uncertainties. The control objective is formulated with weighting functions on the disturbance input and performance output in the framework of the μ-synthesis approach. Our control algorithm can be added on to the collective pitch control as an on-off mechanism with minimal effect on it. Simulation results show that the proposed controller achieves very good robust stability and performance and can significantly reduce blade bending moments.     

Two-layer networked learning control using self-learning fuzzy control algorithms

Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system （NLCS） is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network （RBFNN） is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning （HVAC） system is used to demonstrate the feasibility and effectiveness of the proposed system.     

Improving control and estimation for distributed parameter systems utilizing mobile actuator–sensor network

This paper proposes a scheme for non-collocated moving actuating and sensing devices which is unitized for improving performance in distributed parameter systems. By Lyapunov stability theorem, each moving actuator/sensor agent velocity is obtained. To enhance state estimation of a spatially distributes process, two kinds of filters with consensus terms which penalize the disagreement of the estimates are considered. Both filters can result in the well-posedness of the collective dynamics of state errors and can converge to the plant state. Numerical simulations demonstrate that the effectiveness of such a moving actuator–sensor network in enhancing system performance and the consensus filters converge faster to the plant state when consensus terms are included.     

Review of human–robot coordination control for rehabilitation based on motor function evaluation

As a wearable and intelligent system, a lower limb exoskeleton rehabilitation robot can provide auxiliary rehabilitation training for patients with lower limb walking impairment/loss and address the existing problem of insufficient medical resources. One of the main elements of such a human–robot coupling system is a control system to ensure human–robot coordination. This review aims to summarise the development of human–robot coordination control and the associated research achievements and provide insight into the research challenges in promoting innovative design in such control systems. The patients’ functional disorders and clinical rehabilitation needs regarding lower limbs are analysed in detail, forming the basis for the human–robot coordination of lower limb rehabilitation robots. Then, human–robot coordination is discussed in terms of three aspects: modelling, perception and control. Based on the reviewed research, the demand for robotic rehabilitation, modelling for human–robot coupling systems with new structures and assessment methods with different etiologies based on multi-mode sensors are discussed in detail, suggesting development directions of human–robot coordination and providing a reference for relevant research.     

Active nonlinear partial-state feedback control of contacting force for a pantograph–catenary system

In this paper, a nonlinear partial-state feedback control is designed for a 3-DOF pantograph–catenary system by using backstepping approach, such that the contacting force of the closed-loop system is capable of tracking its reference profile. In the control design, the pantograph–catenary model is transformed into a triangular form, facilitating the utilization of backstepping. Derivatives of virtual controls in backstepping are calculated explicitly. A high-order differentiator is designed to estimate the unknown time derivatives of elasticity coefficient; and an observer is proposed to reconstruct the unmeasurable states. It can be proved theoretically that, with the proposed nonlinear partial-state feedback control, the tracking error of the contacting force is ultimately bounded with tunable ultimate bounds. Theoretical results are demonstrated by numerical simulations.