Nonlinear system-identification of the filling phase of a wet-clutch system

The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation

Fiscal Effects from Privatization: Case of Bulgaria and Poland (Part I)

This study constitutes part of the "Support for Economic Reforms in Bulgaria" project conducted by the Center for Economic and Social Research (CASE Research Foundation), Warsaw and financed by the Open Society Institute, Budapest. The aim of the project is to assist co-operation with Bulgarian counterparts in implementing structural reforms in the Bulgarian economy. At the request of the Bulgarian authorities, this assistance involves developing and carrying out reform programs, as well as evaluating their results in priority areas of structural and institutional reform, with particular reference to the process of ownership transformation. This includes providing an overall strategy for privatization and reporting its effects, monitoring the process of enterprise privatization, post-privatization contract enforcement and the restructuring of newly privatized companies. The purpose of this study is to: - describe and evaluate the fiscal dimension of the privatization process in Bulgaria and Poland, - conduct a cross-country comparison of the fiscal effects of privatization in Bulgaria and Poland, examining their respective approaches to the same, - identify the crucial factors in the privatization strategy and policies of both countries that affect their privatization revenues, - provide background information for the possible transfer of know-how concerning the best approach to maximizing the fiscal effects of privatization, by examining those positive and negative aspects of Poland's experience that could prove relevant to Bulgaria's economic environment. This study includes an evaluation of the fiscal effects of privatization in both countries in the period since the very beginning of the process, i.e. in the case of Poland since 1990 and in the case of Bulgaria since 1993. The crosscountry comparison of the fiscal dimension of privatization has been contingent on the privatization models, priorities and methods applied in both countries.privatization, Bulgaria, Poland, fiscal effect

Switched predictive control design for optimal wet-clutch engagement

Modeling of hydraulic clutch transmissions is far from straightforward due to their nonlinear hybrid dynamics, i.e. switching between three dynamic phases. In this paper we identify a local linear model only for the constrained first phase, based on which a predictive controller is used to track a suitable engagement signal. The robustness of this controller in the latter two phases is guaranteed by making the constraints inactive and pre-tuning the control parameters based on its closed loop formulation and applying robust stability theorem. This controller is then implemented in real-time on a wet-clutch test setup and is shown to achieve optimal engagement

Position and velocity predictions of the piston in a wet clutch system during engagement by using a neural network modeling

Part 11: Engineering Applications of AI and Artificial Neural NetworksInternational audienceIn a wet clutch system, a piston is used to compress the friction disks to close the clutch. The position and the velocity of the piston are the key effectors for achieving a good engagement performance. In a real setup, it is impossible to measure these variables. In this paper, we use transmission torque and slip to approximate the piston velocity and position information. By using this information, a process neural network is trained. This neural predictor shows good forecasting results on the piston position and velocity. It is helpful in designing a pressure profile which can result in a smooth and fast engagement in the future. This neural predictor can also be used in other model-based control techniques

Fine tuning of a wet clutch engagement by means of a genetic algorithm

Part 2: Optimization-Genetic AlgorithmsInternational audienceIn many practical engineering applications, a feed-forward control is often used to control the system with some parameterized signals, for example, a wet clutch system. Usually these signals are designed empirically. In this paper, firstly, genetic algorithm (GA) will be used to optimize parameters. Then by knowing the system response of the test bench in the frequency domain, GA will be used again to fine tuning this parameterized signal. The result is then compared to those performances of using signal without fine tuning step. It is shown that after applying the fine tuning method, the resulted signal can achieve a better performance

Adaptive nonlinear control and state estimation using universal approximators

Thesis (doctoral)--서울대학교 대학원 :전기·컴퓨터공학부,2003.Docto

Pre-stabilized Energy-optimal Model Predictive Control

This paper presents Pre-stabilized Energy-optimal Model Predictive Control which is developed based on the existing Energy-Optimal Model Predictive Control (EOMPC) approach. EOMPC is a control method to realize energyoptimal point-to-point motions within a required motion time. In order to obtain a sufficiently large prediction time horizon with a limited number of decision variables resulting in less computational load and solving the optimization problem within the chosen sampling time, nonequidistant time intervals are used over the prediction horizon. This approach is called blocking. However blocking yields a non-smooth optimal solution and as a result the energy-optimality is only approximately achieved. In order to overcome this drawback, this paper proposes a prestabilization strategy to reduce the computational load of EOMPC. Pre-stabilization uses deadbeat state feedback to modify the system models employed in the formulation of MPC and yields a much sparser optimization problem. The significant advantage of the pre-stabilization on computational speed of MPC optimization problems is clarified. The computational efficiency and performance of EOMPC with pre-stabilization is validated through numerical simulations.status: publishe

Classical and Modern Methods for Time-constrained Energy Optimal Motion - Application to a Badminton Robot

Two control approaches are presented to improve the energy efficiency of a robot which has to perform point-to-point motions during a fixed time interval. The first approach is based on a time-optimal servo control algorithm whose parameters are optimized in order to achieve energy efficient behavior. The second approach is a energy-optimal model predictive control approach. The developed approaches are applied to a robot playing badminton. The robot is still able to intercept most of the opponent shuttles on time, while a significant reduction of the energy consumption is demonstrated in both cases. © 2013 IEEE.status: publishe

Classical and Modern Methods for Time-constrained Energy Optimal Motion - Application to a Badminton Robot

Two control approaches are presented to improve the energy efficiency of a robot which has to perform point-to-point motions during a fixed time interval. One is called Proximate Energy Optimal Servo (PEOS) and the other is called Energy-Optimal Model Predictive Control (EOMPC). The PEOS approach is based on the Proximate Time Optimal Servo (PTOS) control algorithm whose parameters are optimized in order to achieve energy efficient behavior. The EOMPC approach developed based on the Time-Optimal Model Predictive Control (TOMPC) approach achieves energy optimal performance by minimizing the energy losses in the object function. The two developed approaches are applied to a robot playing badminton and compared to two existing control approaches: the Proximate Time Optimal Servo (PTOS) and the Time-OptimalModel Predictive Control (TOMPC). The robot is still able to intercept most of the opponent shuttles on time, while a significant reduction of the energy consumption is demonstrated in both cases.status: publishe