Adaptive Supervisory Control Research Articles

Adaptive Supervisory Control of Automated Manufacturing Systems With Unreliable Resources Based on Smart Switch Controllers

Adaptive Supervisory Control of Automated Manufacturing Systems With Unreliable Resources Based on Smart Switch Controllers

Neural network-based distributed intelligent supervisory control for multi-agent systems with unknown input powers

In this paper, a synchronisation problem is studied for a class of nonlinear leader-following systems. Based on adaptive control with some algebra lemmas, we propose a distributed control scheme. This scheme ensures each follower to asymptotically synchronise with the leader. Compared with the existing works where system input powers are assumed one, the input powers considered in this paper are unknown but larger than one. Based on Graph theory, Lyapunov theory and radial basis function (RBF) networks, we design a distributed adaptive supervisory control method. It is proven that the consensus among the leader and followers are achieved and all the signals including tracking errors asymptotically converge to a small neighbourhood of the origin. Finally, simulation results are displayed to demonstrate the effectiveness of control scheme.

Adaptive supervisory control for automated manufacturing systems using borrowed-buffer slots

Robust deadlock supervisory control techniques for automated manufacturing systems under resource failures that need additional central buffers may lead to supererogatory cost in control implementation. To mitigate this issue, this paper reports a Petri net-based low-cost adaptive supervisory control policy that does not require extra buffers. If an unreliable resource fails, three classes of buffer spaces can be temporarily used to store parts that require the failed resource in their impending processing routes. The proposed adaptive supervisory control comprises of control places and switch controllers. If an unreliable resource fails, the switch controllers are activated to move the part types that require the failed resource in their subsequent processing stages into borrowed buffer spaces. After the failed resource is recovered, the system returns to its normal operating mode. The part types in the borrowed buffer spaces will be returned to their last processing stages before the occurrence of the failure and continued along their processing route. We propose criteria for selecting buffer space borrowers and buffer space lenders. Furthermore, redundant control places are removed by a new technique proposed in the paper in order to reduce the complexity of the supervisory control structure. We demonstrate the proposed method using examples.

Supervisory adaptive control revisited: Linear‐like convolution bounds and tolerance of slow time‐variations

SummarySupervisory control has been shown to be a very effective approach to adaptive control which ensures step‐tracking, exponential stability, and a degree of robustness to unmodeled dynamics. Here we apply the technique to the classical ‐step‐ahead adaptive control problem: we not only prove exponential stability and tracking of a general bounded reference signal, but also a never‐before‐seen linear‐like convolution bound on the effect of the noise/disturbance. This latter property is then leveraged to not only prove robustness to a degree of unmodeled dynamics, but also to a never‐before‐seen tolerance to a degree of nonlinearities and to slow (on average) plant parameter variations.

Adaptive supervisory control for a class of Petri nets with bimodal transitions

Adaptive supervisory control for a class of Petri nets with bimodal transitions

Supervisory Adaptive Control Revisited: Linear-Like Convolution Bounds

Supervisory Adaptive Control Revisited: Linear-Like Convolution Bounds

Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles

In this era of vehicle electrification from mild hybrids to fully electric cars, the importance of fuel economy improvements has led to technological advancements in energy management strategies. The control algorithm is pivotal to the increase in the energy efficiency of a plug-in hybrid system. The existing energy management strategies lack the adaptiveness and utilization of advancements of vehicle-to-vehicle technology. This paper proposes a Cost Optimization for Finite Horizon strategy and also an adaptive version of Equivalent Consumption Minimization Strategy (A-ECMS). The Adaptive-ECMS adds a battery State of Charge (SOC) based reference to ensure the most efficient blended operation for a charge-discharge cycle. The Cost Optimization for Finite Horizon strategy utilizes future driving condition information from vehicle-to-vehicle technology to assist fuel consumption. A forward-looking vehicle propulsion system simulator is developed in Simulink®. A battery model is developed with parameters from the Nickel Cobalt Aluminum chemistry cell. To understand the extent of improvement, the proposed strategies are then compared with the prevalent Finite State Machine strategy (FSM) in three representative driving cycles. The results show an average fuel economy improvement of 5% when compared to the baseline strategy. Among the three strategies, Cost Optimization for Finite Horizon strategy is best suitable for urban driving conditions and Adaptive-ECMS is best suitable for highway driving conditions. For a conventional series-hybrid vehicle, implementing the proposed energy management strategies can help save approximately 8.5 gallons of fuel per year.

Multiple model unfalsified adaptive generalized predictive control based on the quadratic inverse optimal control concept

AbstractUnfalsified adaptive control (UAC) is a class of switching control systems which deals with the control of uncertain systems. The UAC includes a bank of controllers, a supervisor, and a system in which the supervisor selects a stabilizing controller based on the system input and output data. Feasibility is the only assumption required in the UAC strategy, which guarantees that there is at least one stabilizing controller in the controller bank. UAC uses the cost detectability definition to prove closed‐loop stability. The combination of UAC and multiple model supervisory adaptive control (MMASC) results in the proposed unfalsified multi‐model control methodology that enjoys appropriate transient performance and stability proof with required minimum assumptions. In practical controller implementations, the effect of actuator constraints on the control signals is crucial. Despite the significance of constrained systems analysis in real applications, the input constraints in the structures of unfalsified control are not generally considered. Also, the stability analysis of constrained unfalsified control is key to its practical applications. In this article, the input constrained systems are considered using the constrained generalized predictive control (GPC) as the main controllers. Subsequently, to handle virtual signal generation for the GPCs in the UAC context, the inverse optimal control strategy is engaged and formulated to solve the signal generation problem. Simulation results are employed to show the effectiveness of the proposed methodology.

Design on Type-2 Fuzzy-Based Distributed Supervisory Control With Backlash-Like Hysteresis

In this article, the distributed synchronization control problem is investigated for a class of uncertain nonlinear leader-following systems on directed graph, where unknown backlash-like hysteresis exists in the actuator of each follower and the topology of the graph is fixed. Based on basic graph theory, the principle of variable structure control and Lyapunov theory, by using the unknown smooth function approximation capability of type-2 fuzzy logic systems, a new distributed adaptive supervisory control design method is proposed such that all followers asymptotically synchronize to the leader and each synchronizing error is bounded. In the control method, with the help of supervisor control strategy, it is guaranteed that the resulting closed-loop signals including system states, respectively, belong to the corresponding compact sets, which is necessary for fuzzy approximation theory because it is on a compact set that an unknown smooth function can be approximated by fuzzy logic system. Furthermore, the adaptive compensation terms of the optimal approximation errors are adopted to reduce the effects of modeling errors. Finally, simulation results demonstrate the effectiveness of the proposed new design method.

A sustainable and preventative warning strategy for the safe production of underground hydrocarbon storage

Abstract This paper considers safe and clean production operating conditions and is a long-term perspective analysis of underground hydrocarbon storage (UHS) injection-production pressure management. For UHS, efficient preventative warning and safe production practices aim to prevent underground hydrocarbon leakage, ensure cleaner production and promote sustainability. A preventative risk warning strategy is proposed based on adaptive supervisory control and a data acquisition system to enhance the uncertainty risk management and control of current underground hydrocarbon safe production sustainability; the copula approach and a Bayesian inference-based approach with an adaptive neuro-fuzzy inference system (Copula-Bayesian-ANFIS) process hybrid framework is presented, completing the risk early warning and control in three steps. The results show that the pressure-dependent uncertainty risk fluctuation range quantified by the probability interval helps to characterize uncertain parameters related to risk sensitivity under dynamic pressure operation. The three dynamic pressure operating parameters (i.e., casing inner pressure, A annular pressure, and B annular pressure) predicted by this application case with ANFIS exhibit a high coefficient of determination (R2) in a quadratic regression model (0.98398, 0.99139, and 0.98462, respectively). The intensity interval of uncertainty risk mapping is quantitatively described according to the significance level and sensitivity; the perturbation error means obtained by fuzzy reasoning for global pressure load sensitivity are 0.01599 MPa, 0.00355 MPa, and 0.00318 MPa, respectively. In addition, the recommended daily mean injection-production pressure allowable gradient control intervals are −1.8–1.5 MPa, −1.2–1.1 MPa, and −0.4–0.4 MPa, respectively. The uncertainty risk sensitivity quantitative method of the proposed adaptive fuzzy variable enhances the operability of the quantitative description of risk management and sustainable safe production practices. It also has great potential for other types of large-scale underground energy storage systems related to risk control for quantification, characterization, evaluation and safety management.

Performance enhancement of unfalsified adaptive control strategy using fuzzy logic

ABSTRACTUnfalsified Adaptive Switching Supervisory Control (UASSC) is a performance-based data-driven methodology to control uncertain systems with the least possible plant assumptions. There are a set of pre-designed controllers in the controller bank, and the goal is to select the best controller at each time instance. The Multi-Model UASSC (MMUASSC) uses the UASSC concept, but it also benefits from a set of pre-specified models in the model bank. This paper introduces a method to improve the performance of the UASSC and MMUASSC by cost function manipulations and fuzzy logic concepts. To achieve this, fuzzy UASSC and fuzzy MMUASSC methods are introduced. In these methods, a time-varying coefficient, which is the output of a fuzzy system, is used along with the conventional cost functions. The input of this fuzzy system is chosen to properly reflect the performance of the corresponding controller in the controller bank. Using this method, the performance of the outside loop controllers is accurately evaluated, and closed-loop stability proof is provided. Also, as the existence of non-minimum phase controllers is problematic, a solution is provided to handle such cases. Finally, simulation results are used to show the effectiveness of the introduced methods.

A Novel Adaptive Supervisory Controller for Optimized Voltage Controlled Demand Response

Recently, it has been shown that voltage-controlled demand response (VCDR), which is based on the principle of the dependence of the active power of demand on the system voltage, can provide ancillary services to future power systems. Voltage control devices used for VCDR can improve system frequency stability to various extents, depending on their locations, load size and load-voltage dependency in the grid. Thus, designing a supervisory controller to guarantee that such devices are optimally utilized for VCDR is necessary, as this can contribute in enhancing system frequency stability. In this paper, a novel adaptive supervisory controller (ASC) is proposed to optimally use VCDR resources in large power systems for such purposes. To ensure the effective operation of the ASC, an assessment of the impacts of on-load tap changer (OLTC) transformers on the system frequency is essential. In this regard, clustering techniques and principal component regression are used as offline tools to evaluate the influences of OLTCs transformers on VCDR in large-scale power systems using the IEEE 39-bus test system. Also, to estimate the effects of the OLTC transformer clusters on VCDR, a comprehensive sensitivity analysis with respect to the gain of the modified OLTC controllers’ frequency input is conducted.

Supervisory adaptive fault‐tolerant control against actuator failures with application to an aircraft

SummaryIn this paper, a supervisory adaptive fault‐tolerant control scheme is proposed for a class of uncertain nonlinear systems with multiple inputs. The multiple inputs are the outputs of an actuator group that may act either on one control surface or on multiple control surfaces and may fail during operation. With some actuator groups as backups, the supervisory adaptive control includes 2 modes: the adaptive compensation mode and the switching mode. The former is used to compensate for the failure of an actuator group as long as at least one actuator of the group works normally, and the latter, to switch the controller from a failed group to a healthy one when the failure is detected by one of the monitoring functions that are constructed to supervise some variables related to system stability. It is shown that with the proposed scheme, all signals of the closed‐loop system are bounded, and prescribed transient and steady state performance of the tracking error can be guaranteed. An aircraft example is used to demonstrate the application of the proposed scheme.

Optimal model distributions in supervisory adaptive control

Several classes of multi-model adaptive control schemes have been proposed in literature: instead of one single parameter-varying controller, in this adaptive methodology multiple fixed-parameter controllers for different operating regimes (i.e. different models) are utilised. Despite advances in multi-model adaptive control theory, the question of how the synthesis of the pairs model/controller will affect transient and steady-state performance is not completely addressed. In particular, it is not clear to which extent placing the pairs model/controller in a structurally optimal way will result in a relevant improvement of the properties of the switching algorithm. In this study the authors focus on a multi-model unfalsified adaptive supervisory switching control scheme, and they show how the minimisation of a suitable structural criterion can lead to improved performance of the adaptive scheme. The peculiarity of the resulting structural optimality criterion is that the optimisation is carried out so as to optimise the entire behaviour of the adaptive algorithm, i.e. both the learning transient and the steady-state response. This is in contrast to alternative multi-model adaptive control schemes, where special structural optimisation considers only the steady-state ideal response and neglects learning transients. A comparison with respect to model distributions achieved via two structural optimisation criteria is made via a benchmark example.

Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints

One of the major factors that can increase the efficiency of wind turbines (WTs) is the simultaneous control of the different parts in several operating area. The main problem associated with control design in wind generator is the presence of asymmetric in the dynamic model of the system, which makes a generic supervisory control scheme for the power management of WT complicated. Consequently, supervisory controller can be utilized as the main building block of a wind farm controller (offshore), which meets the grid code requirements and can increased the efficiency and protection of WTs in (region II and III) at the same time. This paper proposes a new effective adaptive supervisory controller for the optimal management and protection simultaneously of a hybrid WT, in both regions (II and III). To this end, the second order sliding mode with the adaptive gain super-twisting control law and fuzzy logic control are used in the machine side, batteries side and grid side converters, to achieve four control objectives: (1) control of the rotor speed to track the optimal value; (2) adaptive control (commutative mode) in order to maximum power point tracking (MPPT) or power limit in various regions; (3)regulate the average DC link voltage near to its nominal value;(4) ensure: a smooth regulation with high quality of power supply injected into the grid, a satisfactory power factor correction and a high harmonic performance in relation to the AC source and eliminating the chattering effect. Results of extensive simulation studies prove that the proposed supervisory control system guarantees to track reference signals with a high harmonic performance despite external disturbance uncertainties.

Implementation supervisory controller for hybrid wind microgrid system using adaptive neural MIMO model

This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neuralbased supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

A model-based supervisory energy management strategy for a 12 V vehicle electrical system

This paper describes the development, implementation, and experimental verification of a supervisory energy management strategy for the vehicle electrical system of a passenger car. The control strategy commands the alternator duty cycle such that vehicle fuel economy is optimized whilst the instantaneous load current demand is met and constraints on the system voltage and battery state of charge are satisfied.The work is based on a control-oriented model of the vehicle electrical system, experimentally validated against vehicle data. Then, a constrained global optimal control problem is formulated for the energy management of the electrical system, and analytically solved using the Pontryagin׳s Minimum Principle (PMP). The optimal solution obtained is evaluated for a range of different driving cycles and electrical load current profiles, leading to the formulation of an adaptive supervisory control strategy that is implemented and tested in vehicle.

Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt

Abstract This paper presents an adaptive supervisory controller, based on Pontryagin’s Minimum Principle (PMP), for on-line energy management optimization of a plug-in hybrid electric vehicle. Using minimum driving information, such as the total trip length and the average cycle speed, the proposed algorithm relies on adaptation of the control parameter from state of charge feedback. The proposed strategy is referred in the paper to as Adaptive-PMP (A-PMP). The new controller is applied to a detailed forward vehicle simulator of the plug-in hybrid Chevrolet Volt manufactured by General Motors, where an experimentally validated LG Chem battery model is used. The strategy we propose aims at achieving a blended trajectory of the state of charge to minimize the consumed fuel, resulting in an overall better performance than the actual Charge Depleting/Charge Sustaining (CD/CS) strategy currently used on-board of the vehicle. A comparative analysis of three strategies, i.e., the optimal one (PMP), the proposed one (A-PMP) and the in-vehicle one (CD/CS), is conducted in simulation which shows that improvement above 20% in fuel consumption may be achieved when the proposed algorithm is used instead of the current on-board strategy.

Speed controller design of direct torque control system for induction motors by using adaptive supervisory Fuzzy-CMAC

A model-free approach was used to develop an adaptive supervisory Fuzzy-cerebellar model articulation controller (ASFCMAC) for a direct torque control system for an induction motor without shaft encoder. The two parts of the ASFCMAC are a supervisory controller for limiting tracking error to a bounded range and a Fuzzy-cerebellar model articulation controller subsystem for learning and approximating system dynamics. The ASFCMAC parameters are tuned according to adaptive rules derived from Lyapunov stability theory. Simulations and experimental comparisons with adaptive Fuzzy-cerebellar model articulation controller, adaptive cerebellar model articulation controller, fuzzy logic control, and proportional–integral control show that the proposed ASFCMAC has a superior root mean square error in operation over a wide range of speeds.

Wide-Area Delay-Dependent Adaptive Supervisory Control of Multi-machine Power System Based on Improve Free Weighting Matrix Approach

The paper demonstrates the possibility to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) based adaptive supervisory controller (ASC) which considers the wide-area signal transmission delays. The paper uses an LMI-based iterative nonlinear optimization algorithm to establish a method of designing state-feedback controllers for power systems with a time-varying delay. This method is based on the delay-dependent stabilization conditions obtained by the improved free weighting matrix (IFWM) approach. In the stabilization conditions, the upper bound of feedback signal’s transmission delays is taken into consideration. Combining theoriesof state feedback control and state observer, the ASC is designed and time-delay output feedback robust controller is realized for power system. The ASC uses the input information from Phase Measurement Units (PMUs) in the system and dispatches supplementary control signals to the available local controllers. The design of the ASC is explained in detail and its performance validated by time domain simulations on a New England test power system (NETPS).