Open-loop controller

About: Open-loop controller is a research topic. Over the lifetime, 16148 publications have been published within this topic receiving 224014 citations. The topic is also known as: non-feedback controller & open-loop control law.

Multilevel load-frequency control of interconnected power systems

Abstract: This paper presents a two-level optimal control for the load-frequency control of interconnected power systems using modern optimal control and multilevel system techniques. The controller minimises the deviations in frequency and scheduled tie-line power resulting from sudden disturbances. The interconnected power system considered in the paper is decomposed into subsystems each of which has a first level local controller. The local controller controls each subsystem according to the interaction variables provided by a second level controller (co-ordinator). The co-ordinator improves the interaction variables to satisfy interaction feasibility by means of an iteration technique. This paper differs from previous works in that the proposed controller is fast and simple and usually only a few iterations are required. Using the proposed controller the instability problem of subsystems is eliminated. Moreover, the proposed controller guarantees practically zero steady-state error in both frequency and tie-line power at the final time.

Multirate Minimum Variance Control Design and Control Performance Assessment: A Data-Driven Subspace Approach

Abstract: This paper discusses minimum variance control (MVC) design and control performance assessment based on the MVC-benchmark for multirate systems. In particular, a dual-rate system with a fast control updating rate and a slow output sampling rate is considered, which is not uncommon in practice. A lifted model is used to analyze the multirate system in a state-space framework and the lifting technique is applied to derive a subspace equation for multirate systems. From the subspace equation, the multirate MVC law and the algorithm are developed to estimate the multirate MVC-benchmark variance or performance index. The multirate optimal controller is calculated from a set of input/output (I/O) open-loop experimental data and, thus, this approach is data-driven since it does not involve an explicit model. In parallel, the presented MVC-benchmark estimation algorithm requires a set of open-loop experimental data and close-loop routine operating data. No explicit models, namely, transfer function matrices, Markov parameters, or interactor matrices, are needed. This is in contrast to traditional control performance assessment algorithms. The proposed methods are illustrated through a simulation example

Methodology of FPGA-Based Mathematical Error-Based Tuning Sliding Mode Controller

Abstract: Most of nonlinear controllers need real time mobility operation so one of the most important devices which can be used to solve this challenge is Field Programmable Gate Array (FPGA). FPGA can be used to design a controller in a single chip Integrated Circuit (IC).Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a FPGAbased chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the FPGA-based sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to FPGA-based sliding mode controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor . FPGA-based Chattering free mathematical errorbased tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12). To have higher implementation speed with good performance TVSC is implemented on Spartan 3E FPGA using Xilinx software (controller computation time=30.2 ns, Max frequency=63.7 MHz and controller action frequency=33 MHZ).

Application of Adaptive Sliding Mode Control for Regenerative Braking Torque Control

Abstract: In air hybrid vehicles, there are two independent braking systems: frictional and regenerative. Since the regenerative braking torque is proportional to the parameters such as tank pressure and engine speed, a controller is needed for the control of the regenerative braking torque generated by internal combustion engine, based on the driver preference. In this work, a nonlinear control approach based on adaptive sliding-mode control (ASMC) is employed to tackle the problem of engine torque control during regenerative mode. To this end, a novel mean value model for a recently proposed cam-based air hybrid engine is derived for the regenerative mode and employed for designing the controller. The adaptive sliding-mode controller incorporates the approximately known inverse dynamic model output of the engine as a model-base component of the controller, and an estimated uncertainty term to compensate for the unmodeled dynamics, external disturbances (e.g., gear shifting), and time-varying system parameters such as tank pressure. The robustness and performance of the controller for this particular application is investigated and compared with that of a high-gain PID controller and a smooth sliding-mode controller numerically and experimentally. The results show that the controller performs remarkably well in terms of the robustness, tracking error convergence, and disturbance attenuation. Chattering effect is also removed by utilizing the ASMC scheme.