Optimal Control Of Nonlinear Systems Research Articles

Analytical and Numerical Investigation for the Problem of Optimal Control of Nonlinear System via Quadratic Criteria

Abstract We present a method for synthesis of optimal control with feedback of some class of nonlinear systems via quadratic criteria. This method is based on a special method of successive approximations, whose convergence allows to prove an existence of optimal control and to get the procedure of its construction. The paper examines analytical and numerical investigation of the method and its realization by means of MathCloud system. It presents a numerical experiment to build optimal control of a physical pendulum.

Оптимальное по квадратичному критерию управление нелинейными системами

Оптимальное по квадратичному критерию управление нелинейными системами

Revisiting approximate dynamic programming and its convergence.

Value iteration-based approximate/adaptive dynamic programming (ADP) as an approximate solution to infinite-horizon optimal control problems with deterministic dynamics and continuous state and action spaces is investigated. The learning iterations are decomposed into an outer loop and an inner loop. A relatively simple proof for the convergence of the outer-loop iterations to the optimal solution is provided using a novel idea with some new features. It presents an analogy between the value function during the iterations and the value function of a fixed-final-time optimal control problem. The inner loop is utilized to avoid the need for solving a set of nonlinear equations or a nonlinear optimization problem numerically, at each iteration of ADP for the policy update. Sufficient conditions for the uniqueness of the solution to the policy update equation and for the convergence of the inner-loop iterations to the solution are obtained. Afterwards, the results are formed as a learning algorithm for training a neurocontroller or creating a look-up table to be used for optimal control of nonlinear systems with different initial conditions. Finally, some of the features of the investigated method are numerically analyzed.

Fixed-final-time optimal control of nonlinear systems with terminal constraints

A model-based reinforcement learning algorithm is developed in this paper for fixed-final-time optimal control of nonlinear systems with soft and hard terminal constraints. Convergence of the algorithm, for linear in the weights neural networks, is proved through a novel idea by showing that the training algorithm is a contraction mapping. Once trained, the developed neurocontroller is capable of solving this class of optimal control problems for different initial conditions, different final times, and different terminal constraint surfaces providing some mild conditions hold. Three examples are provided and the numerical results demonstrate the versatility and the potential of the developed technique.

Optimal Flutter Suppression of Nonlinear Typical Wing Section Using Time-Domain Finite Elements Method

AbstractIn this paper, the optimal time-domain finite element method is applied to the flutter suppression of a nonlinear two-dimensional typical wing section. The aeroelastic governing equations are based on the inclusion of stiffness nonlinearity in pitching motion and on the quasi-steady aerodynamics. The flutter suppression problem is formulated as a general optimization problem with equality constraints that are functions of state variables. Using the variational approach, the optimality conditions are derived and the resulting equations are discretized in time-domain. Then, by setting out the discrete equations, a set of nonlinear algebraic equations is generated, and through the Newton–Raphson method, the optimum answer is attained. The numerical results are presented in which the performance of the nonlinear optimal control system designed by the time-domain finite element technique for nonlinear aeroelastic wing sections is illustrated.

Optimal Control with Input Constraints applied to Internal Combustion Engine Test Benches

Optimal control of nonlinear systems provides a major challenge in control engineering. Constraints on the input signals are common to many real-world applications and render the problem to be tackled even more complicated. This paper proposes a method to map the input constraints by nonlinear functions to the state equations of the system, afterwards an approximation of the solution of the resulting optimization problem is calculated by means of a dynamic extension to the state of the system. The approach is applied in the control of test benches for internal combustion engines, where speed and torque references need to be tracked at the crankshaft of the engine. Simulation results using a high-quality simulator, also regarding effects that have not been included in the model for controller design, show the performance of the proposed approach.

A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamics

A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamicsOur aim is to adapt Fletcher's filter approach to solve optimal control problems for systems described by nonlinear Partial Differential Equations (PDEs) with state constraints. To this end, we propose a number of modifications of the filter approach, which are well suited for our purposes. Then, we discuss possible ways of cooperation between the filter method and a PDE solver, and one of them is selected and tested.

A back propagation through time-like min-max optimal control algorithm for nonlinear systems

This paper presents a conjugate gradient-based algorithm for feedback min–max optimal control of nonlinear systems. The algorithm has a backward- in-time recurrent structure similar to the back propagation through time (BPTT) algorithm. The control law is given as the output of the one- layer NN. Main contribution of the paper includes the integration of BPTT techniques, conjugate gradient methods, Adams method for solving ODEs and automatic differentiation, to provide an effective, numerically robust algorithm for solving optimal min–max control problems. The proposed algorithm is evaluated on a robotic system with two DOFs.

Subdivision algorithm for optimal control

SUMMARYWe present a novel algorithm for optimal control of nonlinear systems based on a subdivision algorithm. The algorithm presented in this paper is an alternative to a set‐oriented approach for optimal feedback stabilization. We compare the proposed algorithm to the set‐oriented approach, contrast these two approaches, and use examples to show that the new algorithm produces comparable results. Also, we demonstrate by example that we receive a precomputed optimal solution. The main contribution of the paper is understanding how cost function improves with further subdivision of state space and smaller memory footprint of the final solution in comparison with set‐oriented approach. Copyright © 2012 John Wiley & Sons, Ltd.

A new approach to fuzzy estimation of Takagi–Sugeno model and its applications to optimal control for nonlinear systems

An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi–Sugeno (T–S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T–S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T–S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T–S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters’ weighting method is suggested for tuning T–S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T–S model. Simulation results indicate the potential, simplicity and generality of the algorithm.

New methods for the estimation of Takagi–Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

SUMMARYThis paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T‐S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T‐S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T‐S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T‐S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T‐S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T‐S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use. Copyright © 2011 John Wiley & Sons, Ltd.

A compressed-sensing approach for closed-loop optimal control of nonlinear systems

We present a method that seeks to combine the properties of optimal control with the robust character of closed-loop control. The method relies on the availability of a reduced model of the system to be controlled, in order to express the control problem in a low-dimensional space where the system-state-dependent optimal control law is subsequently approximated in a preprocessing stage. A polynomial expansion is used for the approximation, enabling fast update of the optimal control law each time a new observation of the system state is made available. It results in a real-time compatible, efficient (optimal-like), and robust control strategy. A compressed-sensing approach is proposed to efficiently construct the approximate control law, exploiting the compressible character of the optimal control law in the retained approximation basis. The method is demonstrated for the control of the flow around a cylinder and is shown to perform as well as the much more costly receding-horizon optimal control approach, where the exact optimal control problem is actually recomputed, even in the presence of large aleatoric perturbations. Potential and remaining issues toward application to larger dimensional reduced systems are also discussed, and some directions for improvement are suggested.

Nonlinear optimal control of rotating flexible shaft in active magnetic bearings

The optimal control of nonlinear systems has been studied for years by many researchers. However, the application of optimal control problem to nonlinear non-affine systems needs more attention. In this paper we propose an optimal control design technique for a class of nonlinear and control non-affine equations. The dynamic equations of a flexible shaft supported by a pair of active magnetic bearings (AMBs) are used as the nonlinear control non-affine equations. Mathematical model for the flexible beam is chosen to be the well known Timoshenko beam model, which takes rotary inertia and shear deformations into account, and it is assumed that the shaft is supported by two frictionless bearings at the ends. The effective control of such systems is extremely important for very high angular velocity shafts which are a feature of many modern machines. The control must be able to cope with unbalanced masses and hence be very robust. We shall approach the problem by discretising the Timoshenko beam model and using standard difference formulae to develop a finite-dimensional model of the system. Then we use a recently developed technique for controlling nonlinear systems by reducing the problem to a sequence of linear time-varying (LTV) systems. An optimal control designed for each approximating linear, time-varying system and recent results show that this method will converge uniformly on compact time intervals to the optimal solution.

Fuzzy time-optimal controller (FTOC) for second order nonlinear systems

The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin’s minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher’s interest in fuzzy time-optimal controller design.

A hyperchaotic system stabilization via inverse optimal control and experimental research

In this paper, some basic dynamical properties of a four-dimensional autonomous hyperchaotic system are investigated by means of Poincaré mapping, Lyapunov exponents and bifurcation diagram. The dynamical behaviours of this new hyperchaotic system are proved not only by performing numerical simulation and brief theoretical analysis but also by conducting an electronic circuit experiment. An efficient approaching is developed for global asymptotic stabilization of this four-dimensional hyperchaotic system. Based on the method of inverse optimal control for nonlinear systems, a linear state feedback is electronically implemented. It is remarkably simple as compared with other chaos control ways, like nonlinear state feedback.

구조적 불확실성을 갖는 비선형 시스템의 역최적제어

In this paper, inverse optimal control for nonlinear systems with structural uncertainty is considered. The first, the bounded of structural uncertainty is introduced and based on the control Lyapunov function, a theorem for the globally asymptotic stability is presented. From this a less conservative condition for the inverse optimal control is derived. The result is used to design an inverse optimal controller for a class of nonlinear systems, that improves and extends the existing results. The class of nonlinear system considered is also enlarger. The simulation results show the effectiveness of the method.

Synergizing reinforcement learning and game theory—A new direction for control

Reinforcement learning (RL) has now evolved as a major technique for adaptive optimal control of nonlinear systems. However, majority of the RL algorithms proposed so far impose a strong constraint on the structure of environment dynamics by assuming that it operates as a Markov decision process (MDP). An MDP framework envisages a single agent operating in a stationary environment thereby limiting the scope of application of RL to control problems. Recently, a new direction of research has focused on proposing Markov games as an alternative system model to enhance the generality and robustness of the RL based approaches. This paper aims to present this new direction that seeks to synergize broad areas of RL and Game theory, as an interesting and challenging avenue for designing intelligent and reliable controllers. First, we briefly review some representative RL algorithms for the sake of completeness and then describe the recent direction that seeks to integrate RL and game theory. Finally, open issues are identified and future research directions outlined.

Optimal Control of Nonlinear Multivariable Systems

This paper concerns a study on the optimal control for nonlinear systems. An appropriate alternative in order to alleviate the nonlinearity of a system is the exact linearization approach. In this fashion, the nonlinear system has been linearized using input-output feedback linearization (IOFL). Then, by utilizing the well developed optimal control theory of linear systems, the compensated nonlinear system has been controlled. Thus, the structure of the objective function will be converted into a quadratic form which is qualitativly comparable with usual cost functions, and from operating viewpoint is more favorable. To qualify such a procedure, it has been applied to two minimum and nonminimum-phase chemical processes, and its performance is verified through computer simulations.

Real‐time chaotic circuit stabilization via inverse optimal control

AbstractIn this paper, an efficient approach is developed for real‐time global asymptotic stabilization of the chaotic Chen's circuit, as a typical example for chaotic circuit control. Based on a recently introduced methodology of inverse optimal control for nonlinear systems, a very simple stabilization control law, a linear state feedback, is electronically implemented for the desired global asymptotic stabilization. Both Chen's chaotic system and the designed controller are synthesized and realized by analog electronic components, with the aim of evaluating the physical performance of the real‐time control law and demonstrating the practicality of the control method, which is robust to some input uncertainties. Copyright © 2008 John Wiley & Sons, Ltd.

Combining support vector machines with linear quadratic regulator adaptation for the online design of an automotive active suspension system

As a powerful machine-learning approach to pattern recognition problems, the support vector machine (SVM) is known to easily allow generalization. More importantly, it works very well in a high-dimensional feature space. This paper presents a nonlinear active suspension controller which achieves a high level performance by compensating for actuator dynamics. We use a linear quadratic regulator (LQR) to ensure optimal control of nonlinear systems. An LQR is used to solve the problem of state feedback and an SVM is used to address the question of the estimation and examination of the state. These two are then combined and designed in a way that outputs feedback control. The real-time simulation demonstrates that an active suspension using the combined SVM-LQR controller provides passengers with a much more comfortable ride and better road handling.