Sridhar Seshagiri

Output feedback control of nonlinear systems using RBF neural networks

An adaptive output feedback control scheme is presented for output tracking of a class of continuous-time nonlinear plants. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The efficacy of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower-order networks.

Power Optimization and Control in Wind Energy Conversion Systems Using Extremum Seeking

Power optimization and control for grid-coupled wind energy conversion systems (WECS) has been extensively studied for variable speed wind turbines (VSWTs). However, existing methods are model-based, and are highly dependent on the system dynamics. We employ extremum seeking (ES), which is a non-model-based optimization approach, to perform maximum power point tracking (MPPT), i.e., extract maximum power from WECS in their sub-rated power region. Since the performance of the ES design is related to the system dynamics, we also design a nonlinear controller, based on the field oriented control concept and feedback linearization, that guarantees desired closed-loop performance. The outer ES loop tunes the wind turbine speed to maximize power capture for all wind speeds within the sub-rated power operating conditions. The inner-loop nonlinear control maintains closed-loop performance through a matrix converter, by regulating the electrical frequency and voltage amplitude of the stator of the (squirrel-cage) induction generator. Simulation results are presented to show the effectiveness of the proposed design.

Sliding mode control of F-16 longitudinal dynamics

We consider the application of a conditional integrator based sliding mode control design for robust regulation of minimum-phase nonlinear systems to the control of the longitudinal flight dynamics of an F-16 aircraft. The design exploits the modal decomposition of the linearized dynamics into its short-period and phugoid approximations. The control design is based on linearization, but is implemented on the nonlinear multiple-input multiple-output longitudinal model of the F-16 aircraft. We consider model following for the angle-of- attack, with the regulation of the aircraft velocity (or the Mach- hold autopilot) as a secondary objective. It is shown through extensive simulations that the inherent robustness of the SMC design provides a convenient way to design controllers without gain scheduling, with transient performance that is far superior to that of a conventional gain-scheduled approach with integral control.

Power optimization and control in wind energy conversion systems using extremum seeking

Power optimization and control for grid-coupled wind energy conversion systems (WECS) has been extensively studied for variable speed wind turbines. However, existing methods widely use model-based power optimization algorithms in the outer loop along with linear control techniques in the inner loop. The transient performance of this combination is dependent on the systems operating point, especially under fast varying wind regimes. We employ extremum seeking (ES) in the outer loop, which is a nonmodel-based optimization approach, to perform maximum power point tracking, i.e., extract maximum power from WECS in their subrated power region. Since the convergence rate of the ES design may be limited by the speed of the system dynamics, we also design a nonlinear controller, based on the field-oriented control concept and feedback linearization, that yields improvement in convergence rate by two orders of magnitude. The outer ES loop tunes the turbine speed to maximize power capture for all wind speeds within the subrated power operating conditions. The inner-loop nonlinear control maintains fast transient response through a matrix converter, by regulating the electrical frequency and voltage amplitude of the stator of the (squirrel-cage) induction generator. Simulation results are presented to show the effectiveness of the proposed design.

Power optimization for photovoltaic micro-converters using multivariable gradient-based extremum-seeking

It is well-known that distributed architectures such as micro-converters and micro-inverters for photovoltaic (PV) systems can recover between 10%-30% of annual performance loss or more that is caused by partial shading and/or module mismatch. In this work, we present a novel multivariable gradient-based extremum-seeking (ES) design to extract maximum power from an arbitrary micro-converter configuration of PV modules, that includes cascade and parallel connections. Conventional maximum power point tracking (MPPT) schemes for micro-converters (where each PV module is coupled to its own DC-DC converter) employ a decentralized control, with one peak seeking scheme per each PV module, thereby requiring one control loop and two sensors per module (one each for current and voltage). By contrast, the scheme that we present employs a single control loop with just two sensors, one for the overall array output current and the other one for the DC bus voltage. This centralized design provides more flexibility in tuning the parameters of the controller, and also takes into account interactions between PV modules. The computational effort of our design is not higher than that of the conventional scheme, and simulation results using Simulinks SimPowerSystems toolbox show that our proposed design outperforms the conventional one. Thus, our proposed design offers two benefits: (i) the balance-of-system (BOS) cost reduction as a result of the significantly lower number of sensors, and (ii) improved performance, both contributing towards reduced average cost/watt, and enhancing the economic viability of solar.

Robust control of F-16 lateral dynamics

We consider the application of a conditional integrator based continuous sliding mode control design for robust regulation of MIMO minimum-phase nonlinear systems to the control of the lateral flight dynamics of an F-16 aircraft. The system is non-affine in the input but can be rewritten as the perturbation of a control affine system with matched (input-dependent) disturbances. A parameter dependent transformation brings the system to normal form, for which an output-feedback control can be designed to achieve robust regulation. We provide analytical results for stability, and also show through extensive simulations that the inherent robustness of the SMC design provides a convenient way to design controllers without adaptation for the unknown parameters, with a transient performance that is comparable to discontinuous SMC, but without suffering from the drawback of control chattering.

Longitudinal adaptive control of a platoon of vehicles

A technique for the longitudinal control of a platoon of automated vehicles is presented. A nonlinear model is used to represent the vehicle dynamics of each vehicle within the platoon. The controlled vehicle is assumed to be capable of measuring (or estimating) necessary dynamical information from the vehicle immediately in front of it by its on-board sensors. The computer in the vehicle processes the measured data and generates proper throttling and braking actions to follow the vehicle in front at a safe distance. Simulations are presented for the case of a platoon of four cars following a leader.

Power optimization for photovoltaic micro-converters using multivariable Newton-based extremum-seeking

Extremum seeking (ES) is a real-time optimization technique that has been applied to maximum power point tracking (MPPT) design for photovoltaic (PV) microconverter systems, where each PV module is coupled with its own dc/dc converter. Most of the existing MPPT designs are scalar, i.e., employ one MPPT loop around each converter, and all designs, whether scalar or mutivariable, are gradient based. The convergence rate of gradient-based designs depends on the Hessian, which in turn is dependent on environmental conditions, such as irradiance and temperature. Therefore, when applied to large PV arrays, the variability in environmental conditions and/or PV module degradation results in nonuniform transients in the convergence to the maximum power point (MPP). Using a multivariable gradient-based ES algorithm for the entire system instead of a scalar one for each PV module, while decreasing the sensitivity to the Hessian, does not eliminate this dependence. We present a recently developed Newton-based ES algorithm that simultaneously employs estimates of the gradient and Hessian in the peak power tracking. The convergence rate of such a design to the MPP is independent of the Hessian, with tunable transient performance that is independent of environmental conditions. We present simulation as well as the experimental results that show the effectiveness of the proposed algorithm in comparison with the existing scalar designs, and also to multivariable gradient-based ES.

Optimal PID design for voltage mode control of DC-DC buck converters

This paper presents an LQR-based output-feedback integral control design for voltage regulation of a buck DC-DC converter in continuous-conduction mode (CCM). The averaged state-space model is first transformed to normal form, with the new states being the output and its derivative. A state-feedback LQR design is then designed, following which the derivative term in the control design is approximated using a high-gain observer (HGO). Even without an integrator, i.e., PD control, the voltage error can be practically stabilized, while asymptotic stability is achieved with integral (i.e. PID) control. The design is experimentally verified using the Power Pole board designed by Hirel Systems and the University of Minessota configured as a buck converter, the dSPACE DS1104 board used for the control implementation. The controllers performance is compared against a traditional PID design (based on desired gain-crossover frequency and phase-margin) that is implemented in analog hardware. Simulation and experimental results show that the proposed control method results in satisfactory voltage regulation performance under widely varying input voltage variations and load changes.

Extremum seeking for wind and solar energy applications

Invented in 1922, extremum seeking (ES) is one of the oldest feedback methods. However, its purpose is not regulation but optimization. For this reason, applications of ES have often come from energy systems. The first noted publication on ES in the West is Draper and Lis application to spark timing optimization in internal combustion engines. In the ensuing decades, ES has been applied to gas turbines and even nuclear fusion reactors. Renewable energy applications have brought a new focus on the capabilities of ES algorithms. In this article we present applications of ES in two types of energy conversion systems for renewable energy sources: wind and solar energy. In both areas the goal is maximum power point tracking (MPPT), i.e., the extraction of the maximum feasible energy from the system under uncertainty and in the absence of a priori modeling knowledge about the systems. For the wind energy conversion system (WECS) we perform MPPT by tuning the set point for the turbine speed using scalar ES. For the photovoltaic (PV) array system, we perform MPPT by tuning the duty cycles of the DC/DC converters employed in the system using multivariable ES. For the photovoltaic system we provide experimental results.