Guaranteed cost control via optimal parametric LQ design

Linear Quadratic Optimal Control of a Wave Equation with Boundary Damping and Pointwise Control Input

An adaptive failure compensation control scheme is developed based on linear quadratic (LQ) control for discrete-time possibly nonminimum phase systems with unknown actuator failures with characterizations that some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants. The failure compensation control problem is formulated as a new LQ control problem. Based on some results on new controller structures and design conditions developed in [9], a stable adaptive law is proposed for the estimation of uncertain parameters from both the plant and actuator-failures. The adaptive failure compensation controller is implemented with adaptive parameter estimates, for achieving closed-loop stability and output regulation. Simulation results are presented to verify the desired adaptive control performance in the presence of uncertain actuator failures.

In this work, an Optimal Linear Quadratic (LQR) and optimal Minimax controller is proposed for Wing Rock Motion Control in Delta wing Aircraft model. The nonlinear Wing Rock Motion dynamics were considered. The LQR and Minimax controllers are designed in order to reduce the Wing Rock Motion. Simulations are performed in order to verify the ability of both controllers to reduce the wing rock motion in delta wing aircraft. Results were plotted together in order to show the difference of performance for both controllers for a comparative point of view.

The linear quadratic optimal control problem under quadratic constraints is an optimization problem over a generally non-convex set. Yakubovich [8] and Megretski [4] have studied this problem, and they show how it may be translated into a two-stage, convex optimization problem. In this paper we study the linear quadratic control problem under quadratic constraints for generalized first-order systems. As in the state-space case the linear quadratic control problem without quadratic constraints may be solved in terms of a linear matrix inequality. Subsequently, we use the results from [8] to derive a linear matrix inequality characterizing the linear quadratic optimal behaviour under quadratic constraints.

A stochastic linear quadratic (LQ) control problem is indefinite when the cost weighting matrices for the state and the control are allowed to be indefinite. Indefinite stochastic LQ theory has been extensively developed and has found interesting applications in finance. However, there remains an outstanding open problem, which is to identify an appropriate Riccati-type equation whose solvability is {\it equivalent} to the solvability of the indefinite stochastic LQ problem. This paper solves this open problem for LQ control in a finite time horizon. A new type of differential Riccati equation, called the generalized (differential) Riccati equation, is introduced, which involves algebraic equality/inequality constraints and a matrix pseudoinverse. It is then shown that the solvability of the generalized Riccati equation is not only sufficient, but also necessary, for the well-posedness of the indefinite LQ problem and the existence of optimal feedback/open-loop controls. Moreover, all of the optimal controls can be identified via the solution to the Riccati equation. An example is presented to illustrate the theory developed.

In this letter, we study the irregular output feedback linear quadratic (LQ) control problem where the state is measured through a linear system with noise. It is interesting to show that the irregular output feedback control with the standard average LQ cost is unsolvable even if the state feedback LQ control is solvable, which is completely different from the classical regular output feedback LQ control. In order to guarantee the solvability of the irregular output feedback control, the LQ cost is modified at the terminal time with the expectation of terminal state in this letter. In the framework of the modified cost function, it is shown that the separation principle holds and the explicitly optimal controller is given in the feedback form of the Kalman filtering. In particular, the feedback gain is calculated by two Riccati equations, independently of the Kalman filtering. The key technique is the analytical solution of the forward and backward differential equations (FBDEs). We also emphasize that the optimal controller at the terminal time is required to be deterministic.

A new algorithmic setting is proposed for the discrete-time finite-horizon linear quadratic (LQ) optimal control problem with constrained or unconstrained final state, no matter whether the problem is cheap, singular, or regular. The proposed solution, based on matrix pseudoinversion, is completed and made practically implementable by a nesting procedure for welding optimal subarcs that enables arbitrary enlargement of the control time interval.

This paper analyzes one kind of linear quadratic (LQ) stochastic control problem of forward backward stochastic control system associated with Lévy process. We obtain the explicit form of the optimal control, then prove it to be unique, and get the linear feedback regulator by introducing one kind of generalized Riccati equation. Finally, we discuss the solvability of the generalized Riccati equation, and its existence and uniqueness of the solutions are proved in a special case.

This paper presents the design of a linear quadratic (LQ) optimal controller for a spar-type floating offshore wind turbine (FOWT). The FOWT is exposed to different sea states and constant wind turbulence intensity above rated wind speed. A new LQ control objective is specified for the floater-turbine coupled control, in accordance with standard requirements, to reduce both rotor speed fluctuations and floater pitch motion in each relevant sea state compared with a baseline proportional-integral (PI) controller. The LQ weighting matrices are selected using time series of the wind/wave disturbances generated for the relevant sea states. A linearized state-space model is developed, including the floater surge/pitch motions, rotor speed, collective blade pitch actuation, and unmeasured environmental disturbances. The wind disturbance modeling is based on the Kaimal spectrum and aerodynamic thrust/torque coefficients. The wave disturbance modeling is based on the Pierson–Moskowitz spectrum and linearized Morison equation. A high-fidelity FOWT simulator is used to verify the control-oriented model. The simulation results for the OC3-Hywind FOWT subjected to turbulent wind show that a single LQ controller can yield both rotor speed fluctuation reduction of 32–72% and floater pitch motion reduction of 22–44% in moderate to very rough sea states compared with the baseline PI controller.

A new indirect adaptive control scheme based on recurrent neural networks is proposed. The certainty equivalence principle is used to combine an adaptive law with a control structure derived from the linear quadratic (LQ) control problem. The proposed approach includes two sets of neural networks each with two feedback connected layers to solve for two types of algebraic matrix Riccati equations. One is for the Kalman filter and the other one is for the LQ controller design. The gradient algorithm is used as an adaptive law for identifying plant parameters and is used as the update rule for neural networks.

In this paper, we use the solutions of forward-backward stochastic differential equations with random jumps to get the optimal controls for forward-backward stochastic linear quadratic optimal control problem and the Nash equilibrium point for nonzero sum differential games problem.

Optimal controller properties from closed-loop experiments

In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov Decision Problems (MDPs) with continuous state spaces and therefore we can apply the direct-comparison based optimization approach to solve the problem. We first derive the performance difference formula for the LQ problem by utilizing the state separation property of the system structure. Based on this, we successfully derive the optimality conditions and the stationary optimal feedback control. By introducing the optimization, we establish a general framework for infinite horizon stochastic control problems. The direct-comparison based approach is applicable to both linear and nonlinear systems. Our work provides a new perspective in LQ control problems; based on this approach, learning based algorithms can be developed without identifying all of the system parameters.

Linear quadratic (LQ) controller design is considered for continuous-time systems with harmonic signals of known frequencies and it is shown that the design is reducible to an interpolation problem. All LQ optimal loops are parametrized by a particular solution of this interpolation problem and a (free) stable/proper transfer function. The appropriate choice of this free parameter for optimal stability robustness is formulated as a multiobjective design problem and reduced to a Nevanlinna-Pick interpolation problem with some interpolation points on the boundary of the stability domain. Using a related result from the literature, it is finally shown that, if there is sufficient penalization on the power of the control input, the level of optimum stability robustness achievable with LQ optimal controllers is the same as the level of optimum stability robustness achievable by arbitrary stabilizing controllers.

Stability is the first demand of the feedback control design. The parametrization of all stabilizing controllers is standard theory. The stabilizing controller has some free parameters which can be used for further optimization. Linear Quadratic (LQ) control and Dead Beat control are standard algorithms for optimal control of discrete time models of real systems. The dead beat control results in very large input signals which are not realizable. The LQ control is widely used, because enables to tune the criterion to our requirements. In this paper the combination of both tuning algorithms is presented. The dead beat control algorithm has a unique solution. To enlarge number of steps gives us free parameters which can be used for the optimization of LQ criterion.