Showing papers in "IEEE Transactions on Control Systems and Technology in 2019"

Disturbance-Observer-Based Control for Air Management of PEM Fuel Cell Systems via Sliding Mode Technique

Abstract: In this paper, a model-based robust control is proposed for the polymer electrolyte membrane fuel cell air-feed system, based on the second-order sliding mode algorithm. The control objective is to maximize the fuel cell net power and avoid the oxygen starvation by regulating the oxygen excess ratio to its desired value during fast load variations. The oxygen excess ratio is estimated via an extended state observer (ESO) from the measurements of the compressor flow rate, the load current, and the supply manifold pressure. A hardware-in-loop test bench, which consists of a commercial twin screw air compressor and a real-time fuel cell emulation system, is used to validate the performance of the proposed ESO-based controller. The experimental results show that the controller is robust and has a good transient performance in the presence of load variations and parametric uncertainties.

PDE Model-Based Boundary Control Design for a Flexible Robotic Manipulator With Input Backlash

Abstract: This brief considers the control design problem for a flexible robotic manipulator. Our control strategies are to: 1) move the manipulator to the certain desired angle; 2) suppress the vibration at the neighborhood of the desired angle; and 3) handle the backlash nonlinearity existing in the practical system. Based on the infinite-dimensional dynamic model, a boundary controller with input backlash is designed to achieve the control aims. An output feedback method is adopted to represent a controller to handle the feedback signals that are not able to be measured directly. The effectiveness of the designed controllers and the stability of the system are demonstrated by theoretical analysis, numerical simulations, and physical experiments.

Event-Triggered Finite-Time Integral Sliding Mode Controller for Consensus-Based Formation of Multirobot Systems With Disturbances

Abstract: This paper deals with the distributed fast and finite-time consensus problem of multiagent systems with bounded disturbances in a leader-follower-based frame work. A novel integral sliding mode-based robust finite-time event-triggered control strategy has been proposed. A fast reaching law is also utilized to improve the speed of convergence. The triggering condition is derived based on the defined novel measurement error, suited for systems with model uncertainties/disturbances. The expression for the lower bound for the interexecution time has been derived to ensure that the zeno behavior is avoided. The proposed nonlinear consensus protocol is such that the desired relative state deviation between the agents can be achieved with a directed graph topology. The theoretical results are validated through real-time experimentations done using Pioneer P3-DX as well as FireBird VI robots.

A Robust Consensus Algorithm for Current Sharing and Voltage Regulation in DC Microgrids

Abstract: In this paper, a novel distributed control algorithm for current sharing and voltage regulation in DC microgrids is proposed. The DC microgrid is composed of several distributed generation units, including buck converters and current loads. The considered model permits an arbitrary network topology and is affected by an unknown load demand and modeling uncertainties. The proposed control strategy exploits a communication network to achieve proportional current sharing using a consensus-like algorithm. Voltage regulation is achieved by constraining the system to a suitable manifold. Two robust control strategies of sliding mode type are developed to reach the desired manifold in a finite time. The proposed control scheme is formally analyzed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weighted average of the voltage references.

Trajectory Tracking and Path Following for Underactuated Marine Vehicles

Abstract: In this paper, we present a control strategy for trajectory tracking and path following of generic paths for underactuated marine vehicles. Our work is inspired and motivated by previous works on ground vehicles. In particular, we extend the definition of the hand position point, introduced for ground vehicles, to autonomous surface vehicles and autonomous underwater vehicles, and then use the hand position point as output for a control strategy based on the input–output feedback linearization method. The presented strategy is able to deal with external disturbances affecting the vehicle, e.g., constant and irrotational ocean currents. Using the Lyapunov analysis, we are able to prove that the closed-loop system has an external dynamics that is globally exponentially stable and an internal dynamics that has ultimately bounded states, both for the trajectory tracking and the path following control problems. Finally, we present a simulation case study and experimental results in order to validate the theoretical results.

Distributed Fault-Tolerant Cooperative Control for Multi-UAVs Under Actuator Fault and Input Saturation

Abstract: This paper investigates a difficult problem of distributed fault-tolerant cooperative control for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and input saturation. To eliminate the “explosion of complexity” in the traditional backstepping architecture, the dynamic surface control is utilized to construct the distributed fault-tolerant control scheme. Moreover, by using the disturbance observer technique, actuator faults and external disturbances are estimated as lumped uncertainties. Furthermore, to reduce the adverse influence caused by the control input saturation of UAVs, auxiliary dynamic systems are introduced to regulate the control signals when the input signals are saturated for a long time. The key feature is that the proposed fault-tolerant cooperative controller is designed based on the local information of neighboring UAVs and the factors including actuator faults, input saturation, and external disturbances are simultaneously addressed. It is shown by Lyapunov approach and graph theory that the synchronization tracking stability can be guaranteed and all follower UAVs can track the leader UAV. The effectiveness of the proposed control scheme is further validated by numerical simulation results.

Efficient PID Tracking Control of Robotic Manipulators Driven by Compliant Actuators

Abstract: This brief aims to show that a linear proportional–integral–derivative (PID) controller is theoretically valid for tracking control of robotic manipulators driven by compliant actuators. The control problem is formulated into a three-time-scale singular perturbation formula, including a slow time scale at the rigid robot dynamics, one actual fast time scale at the actuator dynamics, and another virtual fast time scale at the controller dynamics. A PID-type controller is derived to guarantee semiglobal practical exponential stability of the rigid robot dynamics, and a derivative-type controller is applied to establish global exponential stability of the actuator dynamics. Based on a state transformation to the closed-loop rigid robot dynamics and the extended Tikhonov’s theorem, it is proven that the entire system has semiglobal practical exponential stability under a proper choice of control parameters. The proposed controller is not only structurally simple and model-free resulting in low implementation cost, but also robust against external disturbances and parameter variations. The current design is only valid while the spring stiffness is relatively large compared with other parameters of the robot dynamics. Experimental results based on a single-link compliant robotic manipulator have verified effectiveness of the proposed approach.

Driver-Automation Cooperation Oriented Approach for Shared Control of Lane Keeping Assist Systems

Abstract: This paper presents a novel shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision-making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in-the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. The closed-loop stability of the whole driver-vehicle system can be rigorously guaranteed using the Lyapunov stability argument. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers.

Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model

Abstract: This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially nondimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that excluding open-circuit voltage (OCV), there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearized SPM is unique. It is found that the model is unique provided that the electrode OCV functions have a known nonzero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge-transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy data, and show additionally that the parametrized model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between the model and the experiment over a 10-min dynamic discharge.

Path-Following Control of an AUV: A Multiobjective Model Predictive Control Approach

Abstract: The path-following (PF) problem of an autonomous underwater vehicle (AUV) is studied, in which the path convergence is viewed as the main task while the speed profile is also taken into consideration as a secondary task. To accommodate the prioritized PF tasks, a novel multiobjective model predictive control (MPC) (MOMPC) framework is developed. Two methods, namely, weighted sum (WS) and lexicographic ordering, are investigated for solving the MOMPC PF problem. A logistic function is proposed for the WS method in an attempt to automatically select the appropriate weights. The Pontryagin minimum principle is subsequently applied for the WS-MOMPC implementation. The implicit relation between the two methods is shown, and the convergence of the solution with the MOMPC PF control algorithms is analyzed. Simulation studies on the Saab SeaEye Falcon AUV demonstrate the effectiveness of the proposed MOMPC PF control.

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Abstract: Electric vehicle (EV) charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV charging control scheme to achieve “valley-filling” (i.e., flattening demand profile during overnight charging), meanwhile meeting heterogeneous individual charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a nonseparable objective function and strongly coupled inequality constraints. We propose a novel shrunken-primal-dual subgradient algorithm to support the decentralized control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.

Distributed Platoon Control Under Topologies With Complex Eigenvalues: Stability Analysis and Controller Synthesis

Abstract: The platooning of autonomous vehicles can significantly benefit road traffic. Most previous studies on platoon control have only focused on specific communication topologies, especially those with real eigenvalues. This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis. Linear platoon dynamics are derived using an inverse vehicle model compensation, and graph theory is employed to model the communication topology, leading to an integrated high-dimension linear model of the closed-loop platoon dynamics. Using the similarity transformation, a sufficient and necessary condition is derived for the internal stability, which is completely defined in real number field. Then, we propose a Riccati inequality based algorithm to calculate the feasible static control gain. Further, disturbance propagation is formulated as an $\text {H}_{\infty }$ performance, and the upper bound of spacing errors is explicitly derived using Lyapunov analysis. Numerical simulations with a nonlinear vehicle model validate the effectiveness of the proposed methods.

Adaptive Fixed-Time Six-DOF Tracking Control for Noncooperative Spacecraft Fly-Around Mission

Abstract: This brief is devoted to the fixed-time six-DOF tracking control problem for noncooperative spacecraft fly-around mission in the presence of the parameters uncertainties and disturbances. First, a new and coupled six-DOF relative motion dynamic model without using any target orbital information is established. Subsequently, a novel nonsingular fixed-time terminal sliding mode (NFTSM) with bounded convergence time in regardless of the initial states is designed, which not only can circumvent the singularity problem, but also has faster convergence performance than fast terminal sliding mode. By employing the designed NFTSM and the adaptive technique, a continuous adaptive nonsingular fixed-time fast terminal sliding mode control strategy with no information of the mass, inertia matrix, and disturbances is proposed, which can eliminate the chattering phenomenon and guarantee the fixed-time reachability of the relative position and attitude tracking errors into the small regions containing the origin. Finally, the performance of the proposed control schemes is demonstrated by numerical simulations.

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Gauss–Hermite Particle Filter

Abstract: This brief proposes a prediction method of remaining useful life (RUL) based on Gauss–Hermite particle filter (GHPF) in nonlinear and non-Gaussian systems of Lithium-ion batteries (LIBs). In this brief, to improve the accuracy and reduce the computational complexity of the estimation of state of health (SOH), multiscale extended Kalman filter is proposed to execute state of charge (SOC) and SOH joint estimation with dual time scales because of the slow-varying characteristic of SOH and fast-varying characteristic of SOC. Based on the estimation of SOH, a GHPF is developed to update the parameters of the capacity degradation model in real time and predict the RUL of LIBs. The simulation results show that the proposed prediction method of RUL has a better performance and higher precision than the method based on standard PF.

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Abstract: This paper presents a traction control (TC) system for electric vehicles with in-wheel motors, based on explicit nonlinear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times on the order of microseconds. These are significantly lower than the required time step for a TC application. Hence, the explicit model predictive controller can run at the same frequency as a simple TC system based on proportional integral (PI) technology. High-fidelity model simulations provide: 1) a performance comparison of the proposed explicit nonlinear model predictive controller (NMPC) with a benchmark PI-based traction controller with gain scheduling and anti-windup features, and 2) a performance comparison among two explicit and one implicit NMPCs based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit NMPC formulations.

Observer-Based Output Feedback Attitude Stabilization for Spacecraft With Finite-Time Convergence

Abstract: This brief investigates the problem of finite-time output feedback control for spacecraft attitude stabilization without angular velocity measurement. First, two new sufficient conditions for finite-time ultimate boundedness and local finite-time stability are derived, which reduce the conservativeness of the traditional conditions. Then, based on the two new sufficient conditions of the finite-time stability, a finite-time observer is proposed to estimate the unknown angular velocity by using the quadratic Lyapunov function method. Next, a finite-time attitude controller is designed based on the estimate of the angular velocity. The finite-time stability of the entire closed-loop system is analyzed through the Lyapunov approach. The rigorous proof shows that the observation errors and the spacecraft attitude will converge to a residual set of zero in finite time. Numerical simulation results illustrate the effectiveness of the proposed strategy.

Model-Based Battery Thermal Fault Diagnostics: Algorithms, Analysis, and Experiments

Abstract: Safety and reliability remain critical issues for lithium-ion (Li-ion) batteries. Out of many possible degradation modes, thermal faults constitute a significant part of critical causes that lead to battery degradation and failure. Therefore, it is extremely important to diagnose these thermal faults in real time to ensure battery safety. Motivated by this fact, we propose a partial differential equation (PDE) model-based real-time scheme in this paper for diagnosing thermal faults in Li-ion batteries. The objective of the diagnostic scheme is to detect and estimate the size of the thermal fault. We utilize a distributed parameter 1-D thermal model for cylindrical battery cells in conjunction with PDE observer-based techniques to design the scheme. Furthermore, we apply threshold-based technique to ensure robustness against modeling and measurement uncertainties. The effectiveness of the scheme is illustrated by: 1) analytical convergence verification of the PDE observers under heathy and faulty conditions utilizing Lyapunov stability theory; 2) extensive simulation case studies; 3) robustness analysis against model parametric uncertainties; and 4) experimental studies on a commercial Li-ion battery cell.

Adaptive Optimal Control of Heterogeneous CACC System With Uncertain Dynamics

Abstract: Cooperative adaptive cruise control (CACC), as an extension of adaptive cruise control, connects multiple vehicles in a platoon via wireless communication. In practice, different vehicles may have different dynamic parameters and their exact values are unknown/uncertain to designers. In this brief, we propose a new control structure that uses an estimate of dynamic parameters to transform the heterogeneous CACC problem into the regulation problem of error dynamics for each vehicle in the platoon. An adaptive optimal control is proposed to learn the optimal feedback based on online data. The position transfer function between adjacent vehicles is further analyzed in the frequency domain. By sum of squares programming, the minimum headway values that ensure the vehicle string stability are found. Experiments on numerical and complex systems validate our method.

Full-State Tracking Control for Flexible Joint Robots With Singular Perturbation Techniques

Abstract: This paper proposes a practical method to realize multivariable full-state tracking control for industrial robots with elastic joints. Unlike existing methods, the proposed method does not require high-order derivatives of the link states such as acceleration and jerk. Therefore, the proposed method does not suffer from chatter related to inaccurate estimation of high-order derivatives. The method is derived by adopting a singular perturbation technique. A decoupled error dynamics is achieved by two decoupling control loops: a fast loop that controls the deflection error and a slow loop for tracking control on the link side. Our stability analysis based on a linear system shows that the proposed control system is stable as long as the fast system is at least twice as fast as the slow system. A practical method to select the gain is also presented such that the closed-loop poles are placed at the desired locations. In simulation, we compare the proposed method with feedback linearization. The results indicate that in an ideal scenario the proposed method can obtain a similar performance as feedback linearization. However, the proposed method obtains a superior performance in a realistic scenario. A real-world experiment with a six degree-of-freedom commercial industrial robot is carried out to further validate our approach.

Reliable Data Fusion of Hierarchical Wireless Sensor Networks With Asynchronous Measurement for Greenhouse Monitoring

Abstract: This paper investigates the data fusion problem of wireless sensor networks (WSNs) for the greenhouse monitoring system. Considering the characteristics of local consistency and slow change of the greenhouse environmental information, the hierarchical structure of WSNs is proposed for the greenhouse monitoring system, and the two-stage data fusion scheme is presented for the hierarchical network. In the first stage, the weighted data fusion algorithm of WSNs on local state estimation is designed for the cluster, which would improve the fusion accuracy and the ability of anti-interference of the system. Moreover, the multirate measurement mode is proposed to reduce the energy consumption of WSNs under the premise of satisfying the information sensing performance of the system. In the second stage, the data fusion at the sink node is conducted on the support function with the consistency analysis of data from different clusters. The simulation analysis on the greenhouse temperature information is provided to show the effectiveness of the proposed data fusion scheme.

A New Real-Time Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles

Abstract: Based on the equivalent consumption minimization strategy (ECMS), a novel real-time energy management (EM) strategy for parallel hybrid electric vehicles (HEVs) is introduced. Given the full trajectory of the driver demanded power, the ECMS optimal equivalent factor $\lambda ^{*}$ can be determined. For causal EM strategies, the entire drivecycle is not known in advance. Thus, adaptive ECMS (A-ECMS) was introduced, which sets the time-varying equivalent factor $\lambda$ as an estimate of $\lambda ^{*}$ . The proposed EM strategy is an A-ECMS. This EM strategy is designed to catch energy-saving opportunities (CESOs) during the trip, and thus, it is named ECMS-CESO. Since ECMS-CESO eliminates the calculations used for predicting the vehicle velocity and performing horizon optimization, it is easy to implement and fast for real-time applications. Simulation results show that ECMS-CESO yields fuel economy (FE) close to the maximum FE. Compared with an A-ECMS, the proposed strategy improves FE by 7%.

Optimal Containment Control of Unknown Heterogeneous Systems With Active Leaders

Abstract: This brief presents a partially model-free solution to the distributed containment control of multiagent systems using off-policy reinforcement learning (RL). The followers are assumed to be heterogeneous with different dynamics, and the leaders are assumed to be active in the sense that their control inputs can be nonzero. Optimality is explicitly imposed in solving the containment problem to not only drive the agents’ states into a convex hull of the leaders’ states but also minimize their transient responses. Inhomogeneous algebraic Riccati equations (AREs) are derived to solve the optimal containment control with active leaders. The resulting control protocol for each agent depends on its own state and an estimation of an interior point inside the convex hull spanned by the leaders. This estimation is provided by designing a distributed observer for a trajectory inside the convex hull of active leaders. Only the knowledge of the leaders’ dynamics is required by the observer. An off-policy RL algorithm is developed to solve the inhomogeneous AREs online in real time without requiring any knowledge of the followers’ dynamics. Finally, a simulation example is presented to show the effectiveness of the presented algorithm.

Optimal UAV Route Planning for Coverage Search of Stationary Target in River

Abstract: In this brief, we focus on the offline route planning of unmanned aerial vehicle (UAV) for the coverage search mission in a river region. Given the prior likelihood distribution of area importance, this brief aims to generate UAV feasible routes which maximize the cumulative probability of finding a single and stationary target within the required time. First, Gaussian mixture model is used to approximate the prior likelihood distribution, and several river segments with high detection probability corresponding to Gaussian components can be extracted. With the consideration of quantified factors, the river subregions are prioritized by the approximation insertion method and then allocated to UAVs. Moreover, to meet the terminal time constraint, the so-called positive/negative greedy method is proposed to expand or contract waypoints. Finally, the performance of our proposed algorithm is evaluated by simulations on a real river map, and the results verify its good performance in various scenarios.

Optimal Coordination of Automated Vehicles at Intersections: Theory and Experiments

Abstract: In this paper, we present a bilevel, model predictive controller for coordination of automated vehicles at intersections. The bilevel controller consists of a coordination level, where intersection occupancy timeslots are allocated, and a vehicle level, where the control commands for the vehicles are computed. We establish persistent feasibility and stability of the bilevel controller under some mild assumptions and derive conditions under which closed-loop collision avoidance can be ensured with bounded position uncertainty. We thereafter detail an implementation of the coordination controller on a three-vehicle test bed, where the intersection-level optimization problem is solved using a distributed Sequential Quadratic Programing method. We present and discuss results from an extensive experimental campaign, where the proposed controller was validated. The experimental results indicate the practical applicability of the bilevel controller and show that safety can be ensured for large positioning uncertainties.

Robust Sliding Mode Control for Robots Driven by Compliant Actuators

Abstract: Compliant actuators can offer many attractive features over stiff actuators in real human–robot interaction applications, such as low output impedance, smooth force transmission, and shock tolerance. This brief focuses on a robust sliding mode control (SMC) methodology for robotic systems with compliant actuators. First, a continuous SMC design is introduced due to its advantages of strong robustness and chattering attenuation. However, this continuous SMC structure cannot guarantee a high tracking performance in the presence of mismatched disturbances in compliantly actuated robots. Meanwhile, in many application fields, compliantly actuated robots are affected by different kinds of time-varying disturbances, including external environmental disturbances, internal parameter uncertainties, and frictions, which may be in the form of constant, ramp, and parabolic disturbances. To estimate such unknown disturbances, a generalized proportional integral observer (GPIO) technique is employed. By designing a new sliding surface with the help of disturbance estimation, a GPIO-based continuous SMC method is synthesized, which is used to deal with matched/mismatched time-varying disturbances. A detailed stability analysis of the closed-loop system is also presented. Experimental results under three different test conditions are provided to illustrate the promising tracking performance of the proposed control strategy.

Distributed Energy Management for Smart Grids With an Event-Triggered Communication Scheme

Abstract: This paper is concerned with distributed energy management and control issues of both generators and loads. It aims to maximize the total social welfare that balances generation-side expanses, user-side payments, and transmission line costs. A distributed control strategy with continuous information exchange among neighbors is first proposed. It is shown that this distributed algorithm achieves the global optimal power outputs on generators and the optimal electricity usage on loads asymptotically. To reduce communication resource consumptions, the distributed optimization algorithm is further expanded to incorporate event-triggered communication and control mechanism. In this new algorithm, an event-triggering condition for each generator and each load is employed to determine when its related state information should be sampled and transmitted to its neighbors. Compared with the standard periodic sampling and communication schemes, this new distributed and event-triggered algorithm can significantly reduce communication data flows while achieving the nearly identical control performance to that under continuous data communications. The theoretical results of this paper are validated by using a simulation case study with distributed generators and multiple loads on an IEEE 9-bus system.

Information-Based Search for an Atmospheric Release Using a Mobile Robot: Algorithm and Experiments

Abstract: Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring; thus, it has been an active research area for several years known as source term estimation (STE). This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with the low-cost gas sensor responds to the hazardous source simulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for STE based on the mobile robot and the low-cost sensor.

Distributed Economic Dispatch Control via Saddle Point Dynamics and Consensus Algorithms

Abstract: In this brief, a distributed control algorithm is proposed to solve the economic dispatch problem. Without a central control unit, the generators work collaboratively to minimize the generation cost while balancing the supply and demand. The proposed method is based on consensus protocols and the saddle point dynamics. The consensus protocols are employed to estimate the global information in a distributed fashion, and the saddle point dynamics are leveraged to search for the optimal solution of the economic dispatch problem. By utilizing Lyapunov stability analysis, exponential stability of the optimal solution is derived if the capacity limits of the generators are not considered; with the capacity limits, practical stability of the optimal solution is obtained. No global information is needed in the proposed method, and the requirement on initial conditions of the state variables is mild. Several case studies on the IEEE 9-bus and IEEE 118-bus systems are presented to demonstrate the effectiveness of the proposed algorithms.

Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application

Abstract: Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Based on the probabilistic latent variable modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation–maximization Algorithm is introduced for parameter learning of the dynamical probabilistic latent variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic latent variable model.

Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring

Abstract: In order to deeply exploit intrinsic data feature information hidden among the process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components. To reduce the computation complexity in nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated nonlinear system and the benchmark Tennessee Eastman process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.