Javad Mohammadpour

Control of linear parameter varying systems with applications

Part I: Introduction to Modeling and Control of LPV Systems.- An Overview of LPV Systems.- Prediction Error Identification of LPV Systems: Present and Beyond.- Part II: Theoretical Advancements on LPV Control and Estimation.- Parametric Gain-scheduling Control via LPV-stable Realization.- Explicit Controller Parameterizations for Linear Parameter Varying Affine Systems using Linear Matrix Inequalities.- A Parameter-dependent Lyapunov Approach for the Control of Nonstationary LPV Systems.- Generalized Asymptotic Regulation for LPV Systems with Additional Performance Objectives.- Robust Stabilization and Disturbance Attenuation of Switched Linear Parameter Varying Systems in Discrete Time.- Gain-scheduled Output Feedback Controllers with Good Implementability and Robustness.- Decentralized Model Predictive Control of Time-varying Splitting Parallel Systems.- Robust Estimation with Partial Gain-scheduling through Convex Optimization.- Delay-dependent Output Feedback Control of Time-delay LPV Systems.- Part III: Recent Applications of LPV Methods in Control of Complex Systems.- Structured Linear Parameter Varying Control of Wind Turbines.- Attitude Regulation for Spacecraft with Magnetic Actuators: an LPV Approach.- Modeling and Control of LPV Systems: A Vibroacoustic Application.- LPV Modeling and Control of Semi-active Dampers in Automotive Systems.- LPV H[yen] Control for Flexible Hypersonic Vehicles.- Identification of Low-complexity LPV Input-output Models for Control of a Turbocharged Combustion Engines.- Constrained Freeway Traffic Control via Linear Parameter Varying Paradigms.- Linear Parameter Varying Control for the X-53 Active Aeroelastic Wing.- Design of Integrated Vehicle Chassis Control Based on LPV Methods.

A survey on diagnostic methods for automotive engines

Faults affecting automotive engines can potentially lead to increased emissions, increased fuel consumption, or engine damage. These negative impacts may be prevented or at least alleviated if faults can be detected and isolated in advance of a failure. United States Federal and State regulations dictate that automotive engines operate with high-precision onboard diagnosis (OBD) systems that enable the detection of faults, resulting in higher emissions that exceed standard thresholds. In this paper, we survey and discuss the different aspects of fault detection and diagnosis in automotive engine systems. The paper collects some of the efforts made in academia and industry on fault detection and isolation for a variety of component faults, actuator faults, and sensor faults using various data-driven and model-based methods.

Technical communique: Dissipative analysis and control of state-space symmetric systems

The paper addresses the problem of analysis and static output feedback control synthesis for strict quadratic dissipativity of linear time-invariant systems with state-space symmetry. As a particular case of dissipative systems, we consider the mixed Hinfin and positive real performance criterion and we develop an explicit expression for calculating the Hinfin norm of these systems. Subsequently, an explicit parametrization of the static output feedback control gains that solve the mixed Hinfin and positive real performance problem is obtained. Computational examples demonstrate the use and computational advantages of the proposed explicit solutions.

Power flow management of microgrid networks using model predictive control

In this paper, we present a power flow management method for a network of cooperating microgrids within the context of a smart grid by formulating the problem in a model predictive control framework. In order to reliably and economically provide the required power to the costumers, the proposed method enables the network of microgrids to share the power generated from their renewable energy sources and minimize the power needed from the micro gas turbines. To corroborate the viability of the proposed method, we will illustrate simulation results on a model consisting of three microgrids in a network.

Stochastic model predictive control method for microgrid management

This paper presents a stochastic model predictive control method for managing a microgrid. In order to reliably provide the required power for costumers, the proposed method enables the microgrid to use the renewable energy sources as much as possible while keeping the storage device to its maximum state of charge and minimizing the power generated by the micro gas turbine. The performance and effectiveness of the proposed method will be finally illustrated by simulating a microgrid model consisting of three nodes including a renewable generation source and a battery, customers, and a micro gas turbine.

Advances in Haptics, Tactile Sensing, and Manipulation for Robot-Assisted Minimally Invasive Surgery, Noninvasive Surgery, and Diagnosis

The developments of medical practices and medical technologies have always progressed concurrently. The relatively recent developments in endoscopic technologies have allowed the realization of the “minimally invasive” form of surgeries. The advancements in robotics facilitate precise surgeries that are often integrated with medical image guidance capability. This in turn has driven the further development of technology to compensate for the unique complexities engendered by this new format and to improve the performance and broaden the scope of the procedures that can be performed. Medical robotics has been a central component of this development due to the highly suitable characteristics that a robotic system can purport, including highly optimizable mechanical conformation and the ability to program assistive functions in medical robots for surgeons to perform safe and accurate minimally invasive surgeries. In addition, combining the robot-assisted interventions with touch-sensing and medical imaging technologies can greatly improve the available information and thus help to ensure that minimally invasive surgeries continue to gain popularity and stay at the focus of modern medical technology development. This paper presents a state-of-the-art review of robotic systems for minimally invasive and noninvasive surgeries, precise surgeries, diagnoses, and their corresponding technologies.

Efficient modeling and control of large-scale systems

Model Reduction, Large-Scale System Modeling and Applications.- Interpolatory Model Reduction of Large-Scale Dynamical Systems.- Efficient Model Reduction for the Control of Large-Scale Systems.- Dynamics of Tensegrity Systems.- Modeling a Complex Aero-Engine Using Reduced Order Models.- Large-Scale Systems Control and Applications.- Robust Control of Large-Scale Systems: Efficient Selection of Inputs and Outputs.- Decentralized Output-Feedback Control of Large-Scale Interconnected Systems via Dynamic High-Gain Scaling.- Decentralized Output Feedback Guaranteed Cost Control of Uncertain Markovian Jump Large-Scale Systems: Local Mode Dependent Control Approach.- Consensus Based Multi-Agent Control Algorithms.- Graph-Theoretic Methods for Networked Dynamic Systems: Heterogeneity and H2 Performance.- A Novel Coordination Strategy for Multi-Agent Control Using Overlapping Subnetworks with Application to Power Systems.- Distributed Control Methods for Structured Large-Scale Systems.- Integrated Design of Large-Scale Collocated Structural System and Control Parameters Using a Norm Upper Bound Approach.

Identification and Control of an MR Damper With Stiction Effect and its Application in Structural Vibration Mitigation

This paper presents the parameter identification and control of a magnetorheological (MR) damper with stiction effect and its application to seismic protection of a model two-story structure. This semi-active device is utilized to reduce the vibration of the model structure in response to earthquake excitations. First, modified Bingham and LuGre models which consider the stiction effect and the velocity-dependent nature of the damper force are proposed. The parameters of the models are identified by solving a nonlinear optimization problem. The Bingham model is considered because of its simple structure to be used in linear parameter varying (LPV) design framework. The parameter identification is performed while the MR damper is attached to the structure. These models are verified experimentally for different operating conditions showing an acceptable level of accuracy. The subsequent part of the paper addresses the design of different types of controllers to command the MR damper to suppress the structural vibrations of a model building due to earthquake excitations. Two types of controllers are considered in this study: 1) an H∞ inverse control based on the mixed-sensitivity design and 2) a dynamic output-feedback LPV controller. In the former one, an H∞ controller is designed for the linear structure and the modified LuGre-based inverse model is used to determine the required voltage from the commanded force. The LPV controller is designed for the combined structure and MR damper based on the modified Bingham model considering the damper velocity as the scheduling parameter. Both controllers are combined with a classical anti-windup scheme to compensate the effect of the saturation on the control voltage. An optimal passive damping design is also obtained for comparison purposes. The performance of the controllers is compared with the passive damping case and clipped-optimal controller for the El Centro and Northridge earthquake inputs with different intensities. The experimental results show the improved performance of the LPV controller design in terms of the maximum acceleration and the RMS values of the structure response.

Second-Order Sliding Mode Strategy for Air–Fuel Ratio Control of Lean-Burn SI Engines

Higher fuel economy and lower exhaust emissions for spark-ignition engines depend significantly on precise air-fuel ratio (AFR) control. However, the presence of large time-varying delay due to the additional modules integrated with the catalyst in the lean-burn engines is the primary limiting factor in the control of AFR. In this paper, the engine dynamics are rendered into a nonminimum phase system using Padé approximation. A novel systematic approach is presented to design a parameter-varying dynamic sliding manifold to compensate for the instability of the internal dynamics while achieving desired output tracking performance. A second-order sliding mode strategy is developed to control the AFR to remove the effects of time-varying delay, canister purge disturbance, and measurement noise. The chattering-free response of the proposed controller is compared with conventional dynamic sliding mode control. The results of applying the proposed method to the experimental data demonstrate improved closed-loop system responses for various operating conditions.

LPV decoupling and input shaping for control of diesel engines

The paper presents the results of application of linear parameter varying (LPV) decoupling control and a prefilter to improve the tracking performance in the air path of Diesel engines modeled as a quasi-LPV system. The proposed decoupling method benefits the multi-variable control of multiinput multi-output (MIMO) systems with variable operating conditions, variable parameters and nonlinear behavior. The results of this paper illustrate the reduced variability and performance enhancement of the two inputs (EGR valve effective area and VGT effective area) and two outputs (boost pressure and mass air flow) dynamic system of the air path of Diesel engines, where there is a significant coupling in the system dynamics. The proposed design method combines a prefilter (used to shape the reference input) with the LPV feedback control (based on an LPV decoupling method) proposed here to achieve the reference tracking with desired transient performance specifications. The prefilter is designed based on the closed-loop dynamics resulting from the LPV design, and a systematic input shaping prefilter design process is developed. The designed prefilter successfully extends the closed-loop bandwidth. Simulation results demonstrate the effectiveness of the input shaping prefilter. Moreover, the designed prefilter is structurally simple and computationally efficient.