Hassan Zargarzadeh

Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter UAV

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Optimal Control of Nonlinear Continuous-Time Systems in Strict-Feedback Form

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Nonlinear Power Sharing Controller for a Double-Input H-Bridge-Based Buckboost–Buckboost Converter

This paper describes the design of a nonlinear controller with power sharing control capabilities of a double input buckboost-buckboost converter. First, this converter is introduced, and its principles of operation and the equations describing the converter circuit are reviewed. Next, the nonlinear model of the buckboost-buckboost converter is found by averaging the state equations. Then, a Lyapunov-based nonlinear controller, which is adaptive against input voltage and load disturbances (to provide line regulation and load regulation) is proposed. Finally, several simulation and experimental results from a prototype buckboost-buckboost converter operating under the proposed controller are reported to verify the operation of the designed controller.

Multivariable robust optimal PID controller design for a non-minimum phase boiler system using loop transfer recovery technique

LQG/Loop Transfer Recovery (LQG/LTR) technique based on Kalman filter as observer, has some difficulties in facing with non-minimum phase systems. The main problem is that the observer becomes unstable when the compensator recovers the target loop asymptotically. In this paper, we propose an optimal robust recoverable target loops for a boiler system. Using asymptotic time-scale eigenstructure assignment ATEA algorithm a stable observer is designed that makes the target loop recovered. Then, the controller is reduced to a PID controller for experimental purposes. Recoverability of target loop under controller reduction has been analyzed. Numerical results of two controllers are given in time and frequency domain. Comparing with a similar work based on loop-shaping Hinfin approach, LQG/LTR controller shows improvements in energy consumption and time responses.

Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign

This paper presents two neuro-adaptive controllers for a class of uncertain single-input, single-output (SISO) nonlinear non-affine systems with unknown gain sign. The first approach is state feedback controller, so that a neuro-adaptive state-feedback controller is constructed based on the backstepping technique. The second approach is an observer-based controller and K-filters are designed to estimate the system states. The proposed method relaxes a priori knowledge of control gain sign and therefore by utilizing the Nussbaum-type functions this problem is addressed. In these methods, neural networks are employed to approximate the unknown nonlinear functions. The proposed adaptive control schemes guarantee that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Finally, the theoretical results are numerically verified through simulation examples. Simulation results show the effectiveness of the proposed methods.

Optimal adaptive control of nonlinear continuous-time systems in strict feedback form with unknown internal dynamics

A novel approach is proposed for multi-input multi-output (MIMO) optimal adaptive control of nonlinear continuous-time systems in strict feedback form with uncertain internal dynamics. First, it is shown that the optimal adaptive tracking problem of strict feedback systems can be reduced to an optimal regulation problem of affine nonlinear continuous-time systems expressed as a function of tracking error by designing a properly chosen adaptive feedforward control input. Then, an optimal adaptive feedback scheme is introduced for the affine system to estimate the solution of the Hamilton-Jacobi-Bellman (HJB) equation online which becomes the optimal feedback control input for the closed-loop system. A Lyapunov based approach is employed to show that the tracking error converges to zero as well as the cost function estimation and the internal dynamics estimation errors provided the system input is persistently exciting. Finally, numerical results are provided to verify the theoretical results.

State and output feedback-based adaptive optimal control of nonlinear continuous-time systems in strict feedback form

This paper focuses on neural network (NN) based adaptive optimal control of nonlinear continuous-time systems in strict feedback form with known dynamics. A single NN is utilized to learn the infinite horizon cost function which is the solution to the Hamilton-Jacobi-Bellman (HJB) equation in continuous-time. The corresponding optimal control input that minimizes the HJB equation is calculated in a forward-in-time manner without using value and policy iterations. First, the optimal control problem is solved in a generic multi input and multi output (MIMO) nonlinear system in strict feedback form with a state feedback approach. Then, the approach is extended to single input and single output (SISO) nonlinear system in strict feedback form by using output feedback via a nonlinear observer. Lyapunov techniques are used to show that all signals are uniformly ultimately bounded (UUB) and that the approximated control signals approach the optimal control inputs with small bounded error. In the absence of NN reconstruction errors, asymptotic convergence to the optimal control input is demonstrated. Finally, a simulation example is provided to validate the theoretical results for the output feedback controller design.

A new discrete-in-time extremum seeking based technique for maximum power point tracking of photovoltaic systems

This work proposes a novel discrete-in-time extremum seeking based technique for maximum power point tracking of photovoltaic systems. The proposed method operates without any knowledge of the power-current characteristic of the utilized photovoltaic panel. This paper first formulates the maximum power point tracking problem for a general power converter connected to a typical solar panel. Then it introduces the proposed tracker algorithm and explains its operation in detail. Finally, it offers simulation results based on application of the proposed method to a boost converter connected to a BP365 solar panel to verify the theoretical results.

Nonlinear power sharing controller for double-input H-bridge based converters

This work designs a nonlinear controller with power sharing control capability for a double input buck-buckboost converter. First it reviews the principles of operation of the buck-buckboost converter and finds the nonlinear model of this converter. Next, it proposes a Lyapunov based nonlinear controller which is adaptive against input voltage and load disturbances. Finally, it provides sufficient experimental results to verify the operation of the designed controller.

Online near optimal control of unknown nonaffine systems with application to HCCI engines

Multi-input and multi-output (MIMO) optimal control of unknown nonaffine nonlinear systems is a challenging problem due to the presence of control inputs inside the unknown nonlinearity. In this paper, the optimal control of MIMO nonlinear nonaffine discrete-time systems in input-output form is considered when the internal dynamics are unknown. First, the nonaffine nonlinear system is converted into an affine-like equivalent nonlinear system under the assumption that the higher-order terms are bounded. Next, a forward-in-time Hamilton-Jaccobi-Bellman (HJB) equation-based optimal approach is developed to control the affine-like nonlinear system using neural network (NN). To overcome the need to know the control gain matrix of the affine-like system for the optimal controller, an online identifier is introduced. Lyapunov stability of the overall system including the online identifier shows that the approximate control input approaches the optimal control with a bounded error. Finally, the optimal control approach is applied to the cycle-by-cycle discrete-time representation of the experimentally validated HCCI engine which is represented as a nonaffine nonlinear system. Simulation results are included to demonstrate the efficacy of the approach in presence of actuator disturbances.