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Full-Text Articles in Mechanical Engineering
Nonlinear H(Infinity) Missile Longitudinal Autopilot Design With Theta-D Method, Ming Xin, S. N. Balakrishnan
Nonlinear H(Infinity) Missile Longitudinal Autopilot Design With Theta-D Method, Ming Xin, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, a new nonlinear H 1 control technique, called μ¡D H 1 method, is employed to design a missile longitudinal autopilot. The μ ¡D H 1 design has the same structure as that of linear H 1 , except that the two Riccati equations that are part of the solution process are state dependent. The μ ¡D technique yields suboptimal solutions to nonlinear optimal control problems in the sense that it provides an approximate solution to the Hamilton-Jacobi-Bellman (HJB) equation. It is also shown that this method can be used to provide an approximate closed-form solution to the …
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear …
Missile Longitudinal Autopilot Design Using A New Suboptimal Nonlinear Control Method, Ming Xin, S. N. Balakrishnan
Missile Longitudinal Autopilot Design Using A New Suboptimal Nonlinear Control Method, Ming Xin, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A missile longitudinal autopilot is designed using a new nonlinear control synthesis technique called the θ-D approximation. The particular θ-D methodology used is referred to as the θ-D H2 design. The technique can achieve suboptimal closed-form solutions to a class of nonlinear optimal control problems in the sense that it solves the Hamilton-Jacobi-Bellman equation approximately by adding perturbations to the cost function. An interesting feature of this method is that the expansion terms in the expression for suboptimal control are nothing but solutions to the state-dependent Riccati equations associated with this class of problems. The θ-D H2 design has the …
Nonlinear H Infinity Missile Longitudinal Autopilot Design With Θ-D Method, Ming Xin, S. N. Balakrishnan
Nonlinear H Infinity Missile Longitudinal Autopilot Design With Θ-D Method, Ming Xin, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, a new nonlinear control synthesis technique, the theta- D method, is employed to design a missile longitudinal autopilot. The θ-D technique yields suboptimal solutions to nonlinear optimal control problems in the sense that it provides approximate solution to the Hamilton-Jacobi-Bellman (HJB) equation. Semi-global asymptotic stability can be achieved by manipulating the perturbation terms which are added to the cost function in developing a series solution. Furthermore, this method can be used to provide an approximate closed-form solution to the state dependent Riccati equation. The particular θ-D methodology adopted in this paper is referred to as θ-D H …
Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin
Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A robust state dependent Riccati equation based guidance/control is investigated in this study. In order to have a better design tool in terms of required interceptor accelerations, the target intercept geometry is formulated in a set of polar coordinates. With this formulation, we formulate a cost function with state dependent weights. In this study, we investigate the effects of such cost functions on the levels of interceptor accelerations. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the target acceleration. In this manner, we will not need target acceleration estimation explicitly in …
Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang
Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang
Mechanical and Aerospace Engineering Faculty Research & Creative Works
We present a new optimal control approach to robust control of robot manipulators in the framework of state dependent Riccati equation (SDRE) technique. To treat this highly nonlinear control system, we formulate it as a nonlinear optimal regulator problem. SDRE technique was used to synthesize an optimal controller to this class of robot control problem. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the parameter uncertainties. A typical two-link robot position control problem was studied to show the effectiveness of SDRE approach and robust extra control design to robotic application.
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Methods for estimating the aerospace system parameters and controlling them through two neural networks are presented in this study. We equate the energy function of Hopfield neural network to integral square of errors in the system dynamics and extract the parameters of a system. Parameter convergence is proved. For control, we equate the equilibrium status of a "modified" Hopfield neural network to the steady state Riccati solution with the system parameters as inputs. Through these two networks, we present the online identification and control of an aircraft using its nonlinear dynamics.
A Class Of Modified Hopfield Networks For Control Of Linear And Nonlinear Systems, Jie Shen, S. N. Balakrishnan
A Class Of Modified Hopfield Networks For Control Of Linear And Nonlinear Systems, Jie Shen, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper presents a class of modified Hopfield neural networks (MHNN) and their use in solving linear and nonlinear control problems. This class of networks consists of parallel recurrent networks which have variable dimensions that can be changed to fit the problems under consideration. It has a structure to implement an inverse transformation that is essential for embedding optimal control gain sequences. Equilibrium solutions are discussed. Numerical results for a motivating aircraft control problem (linear) are presented. Furthermore, we formulate the state-dependent Riccati equation method (SDRE) for a class of nonlinear dynamical system and show how MHNN provides the solution. …