Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Sparse representation based on projection method in online least squares support vector machines

A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines （LS-SVM）. The new inputs are projected into the subspace spanned by previous basis vectors （BV） and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision.

An Improved Approach to Test Diagnosability of Bounded Petri Nets

For bounded Petri nets, Cabasino et al. propose a diagnosability test method that is based on the analysis of a modified basis reachability graph and a basis reachability diagnoser. However, its complexity is exponential in the number of nodes of the basis reachability diagnoser. In order to reduce the complexity of their method, this paper presents a new diagnosability test approach for bounded Petri nets. We present the concept of an extended basis reachability graph and prove that our approach is of polynomial complexity in the number of nodes of extended basis reachability graphs. An example is given to illustrate the application of the presented approach. © 2017 Chinese Association of Automation.

Robust stability analysis of networked control systems

This paper is concerned with the problem of robust stability analysis for networked control systems （NCSs）. A new NCS model is proposed under consideration of both the network-induced delay and parameter uncertainties. The parameter uncertainties appearing in NCSs are norm-bounded, and possibly time-varying. The conventional method and the descriptor system method are used to obtain maximum allowable delay bound （MADB） guaranteeing robust stability and stability of the NCSs, respectively, where the stability criteria are formulated in terms of linear matrix inequalities （LMIs）. And the MADB can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to illustrate the effectiveness of the proposed method.

Robust stabilization for a class of nonlinear networked control systems

The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs with parameter uncertainty, network-induced delay, nonlinearity and data packet dropout in the transmission, a strict linear matrix inequality （LMI） criterion is proposed for robust stabilization of the uncertain nonlinear NCSs based on the Lyapunov stability theory. The maximum allowable transfer interval （MATI） can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to demonstrate the applicability of the proposed algorithm.

Indirect Adaptive Robust Trajectory Tracking Control of Hard Rock TBM with Load Variation of Tunneling Face

Posture adjustment of open-type hard rock tunnel boring machine(TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper,the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters and uncertain loads from complex working conditions in the dynamic model. An indirect adaptive robust control strategy is applied to achieve precise position and posture trajectory tracking control. Simulation results show when the position deviation only occurs in Y-axis and the current orientation is parallel with the designed axis, the deviation can be corrected by controlling the pressure of gripper cylinder and the actual trajectory meets the designed axis when TBM is pushed forward 0.14 m in X-axis. If the deviation only occurs in Z-axis, then the deviation can be corrected by controlling torque cylinders. If the position deviation occurs both in Y-axis and Z-axis at the same time, the pressure of gripper cylinder and torque cylinders should be controlled at the same time to rectify the deviation. Simulation results are shown to illustrate the e ectiveness and robustness of the proposed controller. This research proposes an indirect adaptive robust controller that can track the planned tracking trajectory smoothly and rapidly.

Polyhedral Feasible Set Computation of MPC-Based Optimal Control Problems

Feasible sets play an important role in model predictive control(MPC) optimal control problems(OCPs). This paper proposes a multi-parametric programming-based algorithm to compute the feasible set for OCP derived from MPC-based algorithms involving both spectrahedron(represented by linear matrix inequalities) and polyhedral(represented by a set of inequalities) constraints. According to the geometrical meaning of the inner product of vectors, the maximum length of the projection vector from the feasible set to a unit spherical coordinates vector is computed and the optimal solution has been proved to be one of the vertices of the feasible set. After computing the vertices,the convex hull of these vertices is determined which equals the feasible set. The simulation results show that the proposed method is especially efficient for low dimensional feasible set computation and avoids the non-unicity problem of optimizers as well as the memory consumption problem that encountered by projection algorithms.

Mixed L_1/H-infinity control for uncertain linear singular systems

The mixed L1/H-infinity control problem for a class of uncertain linear singular systems is considered using a matrix inequality approach. The purpose is to design a state feedback control law such that the resultant closed-loop system is regular, impulse-free, stable and satisfies some given mixed L1/H-infinity performance. A sufficient condition for the existence of such control law is given in terms of a set of matrix inequalities by the introduction of inescapable set and ＊-norm. When these matrix inequalities are feasible, an explicit expression of the desired state feedback control law is given. A numerical example is used to demonstrate the applicability of the proposed approach.

Robust D stabilization of singular systems with polytopic uncertainties

The robust D stabilization problem is considered for singular systems with polytopic uncertainties in this paper.Both the derivative matrix E and the state matrix A are with uncertainties,which were not considered before.First,with the introduction of some free matrices,a necessary and sufficient condition for the singular system to be D stable is proposed,based on which,the robust D stable problem is solved,and a sufficient condition for the closed system to be robust D stabilizable is obtained.The desired state feedback controller is given in an explicit expression.Numerical examples show the efficiency of the proposed approach.

Rail pressure controller design of GDI basing on predictive functional control

Gasoline direct injection(GDI)is a pivotal technique for a highly efficient engine.However,how to maintain a stable rail pressure which offers good fuel economy and low emissions,is still a challengeable work.In this paper,a rail pressure controller is designed basing on predictive functional control(PFC),a model predictive control(MPC)method,to surmount the nonlinearity and disc on tinuity brought by the common rail pressure system(CRPS).A control-oriented piecewise linear model is prese nted to simplify the CRPS.The simulation results on a benchmark show that rail pressure tracks the setpoint accurately even with some perturbations.Profiting from the conciseness of PFC algorithm,the controller can compute the online solution in a short time,which makes it possible to realize the strategy on a fast response system.

Importance-Weighted Transfer Learning for Fault Classification under Covariate Shift

In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.

Distributed event-triggered adaptive control for second-order nonlinear uncertain multi-agent systems

In this paper,the event-triggered consensus control problem for nonlinear uncertain multi-agent systems subject to unknown parameters and external disturbances is considered.The dynamics of subsystems are second-order with similar structures,and the nodes are connected by undirected graphs.The event-triggered mechanisms are not only utilized in the transmission of information from the controllers to the actuators,and from the sensors to the controllers within each agent,but also in the communication between agents.Based on the adaptive backstepping method,extra estimators are introduced to handle the unknown parameters,and the measurement errors that occur during the event-triggered communication are well handled by designing compensating terms for the control signals.The presented distributed event-triggered adaptive control laws can guarantee the boundness of the consensus tracking errors and the Zeno behavior is avoided.Meanwhile,the update frequency of the controllers and the load of communication burden are vastly reduced.The obtained control protocol is further applied to a multi-input multi-output second-order nonlinear multi-agent system,and the simulation results show the effectiveness and advantages of our proposed method.

Robust H-infinity control for uncertain discrete-time Markovian jump systems with actuator saturation

The design of robust H-infinity controller for uncertain discrete-time Markovianjump systems with actuator saturation is addressed in this paper. The parameter uncertainties are assumed to be norm-bounded. Linear matrix inequality （LMI） conditions are proposed to design a set of controllers in order to satisfy the closed-loop local stability and closed-loop H-infinity performance. Using an LMI approach, a set of state feedback gains is constructed such that the set of admissible initial conditions is enlarged and formulated through solving an optimization problem. A numerical example is given to illustrate the effectiveness of the proposed methods.

Stability analysis for discrete linear systems with state saturation by a saturation-dependent Lyapunov functional

This paper concerns the stability analysis problem of discrete linear systems with state saturation using a saturation-dependent Lyapunov functional. We introduce a free matrix characterized by the sum of the absolute value of each elements for each row less than 1, which makes the state with saturation constraint reside in a convex polyhedron. A saturation-dependent Lyapunov functional is then designed to obtain a sufficient condition for such systems to be globally asymptotically stable. Based on this stability criterion, the state feedback control law synthesis problem is also studied. The obtained results are formulated in terms of bilinear matrix inequalities that can be solved by the presented iterative linear matrix ineoualitv algorithm. Two numerical examoles are used to demonstrate the effectiveness of the nronosed method_

Kinetic model for hydroisomerization reaction of C_(8)-aromatics

Based on the reported reaction networks,a novel six-component hydroisomerization reaction net-work with a new lumped species including C_(8)-naphthenes and C_(8)-paraffins is proposed and a kinetic model for a commercial unit is also developed.An empirical catalyst deactivation function is incorporated into the model accounting for the loss in activity because of coke forma-tion on the catalyst surface during the long-term opera-tion.The Runge-Kutta method is used to solve the ordinary differential equations of the model.The reaction kinetic parameters are benchmarked with several sets of balanced plant data and estimated by the differential vari-able metric optimization method(BFGS).The kinetic model is validated by an industrial unit with sets of plant data under different operating conditions and simulation results show a good agreement between the model predic-tions and the plant observations.

Reliable H-infinity filtering for linear systems with sensor saturation

In this paper, a new reliable H-infinity filter design problem is proposed for a class of continuous-time sys- tems with sensor saturation and failures. Attention is focused on the analysis and synthesis problems of a full order reliable H-infinity filter such that the filtering error dynamics is asymptotically stable with a guaranteed disturbance rejection atten- uation level -y. It is shown that the filtering error dynamics obtained from the original system plus the filter can be modeled by a linear system with sector bounded nonlinearity. The design conditions are given in terms of solutions to a set of linear matrix inequalities （LMIs）. These conditions are then considered in a convex optimization problem with LMIs constraints in order to design an optimal reliable H-infinity filter. A numerical example is given to illustrate the effectiveness of the proposed results.

Immersion and invariance adaptive control of a class of continuous stirred tank reactors

An immersion and invariance （l＆l） manifold based adaptive control algorithm is presented for a class of continuous stirred tank reactors （CSTR） to realize performance-oriented control in this paper. The nonlinear contraction method is combined into the control law design to render the closed-loop CSTR system globally asymptotically stable, firstly. Then, the l＆l method is used to form the adaptation law such that the off-the-manifold coordinate （the parameter estimation error） converges to zero using P-monotone property enforced by selecting tuning function in manifold. As a result, the state of the closed-loop CSTR converges to its desired value asymptotically. The simulation is given to illustrate the effectiveness of the presented algorithm.

Robust immersion and invariance adaptive coordinated control for a class of biaxial gantry systems

This paper proposes a robust Immersion and Invariance(I&I)adaptive coordinated controller for a class of uncertain linear-motor-driven biaxial gantry system subject to external disturbances for high-accuracy contour tracking.Firstly,the dynamic model of the gantry system is transformed into task coordinate frame,through which the contour tracking can be regarded as a regulation problem.Based on the transformed system dynamics,an I&I-based adaptation law with smooth projection is proposed to estimate the unknown parameters.Different from traditional adaptive control,the proposed robust I&I adaptive control introduces a new term called tuning function in adaptation law to shape the dynamic behaviour of the estimation vector.Then the stability of the closed-loop system is proved by Lyapunov theory.Finally,comparative experiments are executed on an industrial biaxial gantry system with two different cases to verify the effectiveness of the proposed control law.