Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

Evolutionary and Reinforcement Fuzzy Control

Many modern and classical techniques exist for the design of control systems. However, many real world applications are inherently complex and the application of traditional design and control techniques is limited. In addition, no single design method exists which can be applied to all types of system. Due to this 'deficiency', recent years have seen an exponential increase in the use of methods loosely termed 'computational intelligent techniques' or 'soft- computing techniques'. Such techniques tend to solve problems using a population of individual elements or potential solutions or the flexibility of a network as opposed to using a rigid, single point of computing. Through use of computational redundancies, soft-computing allows unmatched tractability in practical problem solving. The intelligent paradigm most successfully applied to control engineering, is that of fuzzy logic in the form of fuzzy control. The motivation of using fuzzy control is twofold. First, it allows one to incorporate heuristics into the control strategy, such as the model operator actions. Second, it allows nonlinearities to be defined in an intuitive way using rules and interpolations. Although it is an attractive tool, there still exist many problems to be solved in fuzzy control. To date most applications have been limited to relatively simple problems of low dimensionality. This is primarily due to the fact that the design process is very much a trial and error one and is heavily dependent on the quality of expert knowledge provided by the operator. In addition, fuzzy control design is virtually ad hoc, lacking a systematic design procedure. Other problems include those associated with the curse of dimensionality and the inability to learn and improve from experience. While much work has been carried out to alleviate most of these difficulties, there exists a lack of drive and exploration in the last of these points. The objective of this thesis is to develop an automated, systematic procedure for optimally learning fuzzy logic controllers (FLCs), which provides for autonomous and simple implementations. In pursuit of this goal, a hybrid method is to combine the advantages artificial neural networks (ANNs), evolutionary algorithms (EA) and reinforcement learning (RL). This overcomes the deficiencies of conventional EAs that may omit representation of the region within a variable's operating range and that do not in practice achieve fine learning. This method also allows backpropagation when necessary or feasible. It is termed an Evolutionary NeuroFuzzy Learning Intelligent Control technique (ENFLICT) model. Unlike other hybrids, ENFLICT permits globally structural learning and local offline or online learning. The global EA and local neural learning processes should not be separated. Here, the EA learns and optimises the ENFLICT structure while ENFLICT learns the network parameters. The EA used here is an improved version of a technique known as the messy genetic algorithm (mGA), which utilises flexible cellular chromosomes for structural optimisation. The properties of the mGA as compared with other flexible length EAs, are that it enables the addressing of issues such as the curse of dimensionality and redundant genetic information. Enhancements to the algorithm are in the coding and decoding of the genetic information to represent a growing and shrinking network; the defining of the network properties such as neuron activation type and network connectivity; and that all of this information is represented in a single gene. Another step forward taken in this thesis on neurofuzzy learning is that of learning online. Online in this case refers to learning unsupervised and adapting to real time system parameter changes. It is much more attractive because the alternative (supervised offline learning) demands quality learning data which is often expensive to obtain, and unrepresentative of and inaccurate about the real environment. First, the learning algorithm is developed for the case of a given model of the system where the system dynamics are available or can be obtained through, for example, system identification. This naturally leads to the development of a method for learning by directly interacting with the environment. The motivation for this is that usually real world applications tend to be large and complex, and obtaining a mathematical model of the plant is not always possible. For this purpose the reinforcement learning paradigm is utilised, which is the primary learning method of biological systems, systems that can adapt to their environment and experiences, in this thesis, the reinforcement learning algorithm is based on the advantage learning method and has been extended to deal with continuous time systems and online implementations, and which does not use a lookup table. This means that large databases containing the system behaviour need not be constructed, and the procedure can work online where the information available is that of the immediate situation. For complex systems of higher order dimensions, and where identifying the system model is difficult, a hierarchical method has been developed and is based on a hybrid of all the other methods developed. In particular, the procedure makes use of a method developed to work directly with plant step response, thus avoiding the need for mathematical model fitting which may be time-consuming and inaccurate. All techniques developed and contributions in the thesis are illustrated by several case studies, and are validated through simulations

Study on identification of nonlinear systems using Quasi-ARX models

制度:新 ; 報告番号:甲3660号 ; 学位の種類:博士(工学) ; 授与年月日:2012/9/15 ; 早大学位記番号:新6026Waseda Universit

A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approac

Neurofuzzy control to address stochastic variation in actuated-coordinated systems at closely-spaced intersections

This dissertation documents a method of addressing stochastic variation at closely-spaced signalized intersections using neurofuzzy control. Developed on the conventional actuated-coordinated control system, the neurofuzzy traffic signal control keeps the advantage of the conventional control system. Beyond this, the neurofuzzy signal control coordinates the coordinated phase with one of the non-coordinated phases with no reduction of the green band assigned to the coordination along the arterial, reduces variations of traffic signal times in the cycle caused by early return to green , hence, makes more sufficient utilization of green time at closely-spaced intersections. The neurofuzzy signal control system manages a non-coordinated movement in order to manage queue spillbacks and variations of signal timings.Specifically, the neurofuzzy controller establishes a secondary coordination between the upstream coordinated phase (through phase) and the downstream non-coordinated phase (left turn phase) based on real-time traffic demand. Under the fuzzy logic signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and hence, less possibly be blocked on the downstream left turn lane. This secondary coordination favors left turn progression and, hence, reduces the queue spillbacks. The fuzzy logic method overcomes the natural disadvantage of currently widely used actuated-coordinated traffic signal control in that the fuzzy logic method could coordinate a coordinated movement with a non-coordinated movement. The experiment was conducted and evaluated using a simulation model created using the microscopic simulation program - VISSIM.The neurofuzzy control algorithm was coded with MATLAB which interacts with the traffic simulation model via VISSIM\u27s COM interface. The membership functions in the neurofuzzy signal control system were calibrated using reinforcement learning to further the performance. Comparisons were made between the trained neurofuzzy control, the untrained neurofuzzy control, and the conventional actuated-coordinated control under five different traffic volumes. The simulation results indicated that the trained neurofuzzy signal control outperformed the other two for each traffic case. Comparing to the conventional actuated-coordinated control, the trained neurofuzzy signal control reduced the average delay by 7% and the average number of stops by 6% under the original traffic volume; as traffic volume increasing to 120%, the reductions doubled