This paper proposes a TS recurrent fuzzy neural network (RFNN) and a robust RFNN control of uncertain nonlinear time-delay systems. First, the TS-RFNN is proposed to learn complex functions with delays. Next, the robu...
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This paper proposes a TS recurrent fuzzy neural network (RFNN) and a robust RFNN control of uncertain nonlinear time-delay systems. First, the TS-RFNN is proposed to learn complex functions with delays. Next, the robust adaptive TS-RFNN control is developed for time-delay nonlinear systems. The advantages of the proposed controller includes: i) asymptotic stability independent on the delay; ii) more simple and legible gain design; and iii) simpler structure of FNN (fewer fuzzy rules). Simulation results demonstrate the validity of the proposed control scheme
This paper considers variations on an extremely simple form of car racing, the challenge being to visit as many way-points as possible in a fixed amount of time. The simplicity of the models enables a very thorough ev...
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This paper considers variations on an extremely simple form of car racing, the challenge being to visit as many way-points as possible in a fixed amount of time. The simplicity of the models enables a very thorough evaluation of various learning algorithms and control architectures, and enables other researchers to work on the same models with relative ease. The models are used to compare the performance of various hand-programmed controllers, and neural networks trained using evolution, and using temporal difference learning. Comparisons are also made between state-based and action-based controller architectures. The best controllers were obtained using evolution to learn the weights of state-evaluation neural networks, and these were greatly superior to human drivers
Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track repres...
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Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human player. This requires a way to create accurate models of players' driving styles, as well as a tentative definition of when a racing track is fun, both of which are provided. We believe this approach opens up interesting new research questions and is potentially applicable to commercial racing games.
Local dominance has been shown to improve significantly the overall performance of multiobjective evolutionary algorithms (MOEAs) on combinatorial optimization problems. This work proposes the control of dominance are...
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Local dominance has been shown to improve significantly the overall performance of multiobjective evolutionary algorithms (MOEAs) on combinatorial optimization problems. This work proposes the control of dominance area of solutions in local dominance MOEAs to enhance Pareto selection aiming to find solutions with high convergence and diversity properties. We control the expansion or contraction of the dominance area of solutions and analyze its effects on the search performance of a local dominance MOEA using 0/1 multiobjective knapsack problems. We show that convergence of the algorithm can be significantly improved while keeping a good distribution of solutions along the whole true Pareto front by using local dominance with expansion of dominance area of solutions. We also show that by controlling the dominance area of solutions dominance can be applied within very small neighborhoods, which reduces significantly the computational cost of the local dominance MOEA
Efficient tool management is very important for the productivity in flexible manufacturing systems. This paper proposes an ant colony approach to minimize the number of tool switching instants in flexible manufacturin...
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Efficient tool management is very important for the productivity in flexible manufacturing systems. This paper proposes an ant colony approach to minimize the number of tool switching instants in flexible manufacturing systems for the first time. The proposed approach is compared to optimal results from the literature, and very promising results are reported.
This paper presents our ongoing work on developing distributed control methods for large-scale groups of autonomous vehicles. We introduce the ideas that we are pursuing, and summarize the trajectory generation algori...
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This paper presents our ongoing work on developing distributed control methods for large-scale groups of autonomous vehicles. We introduce the ideas that we are pursuing, and summarize the trajectory generation algorithm that will be used to model and simulate the moving vehicles. Experimental results for the algorithm are also offered in the paper.
The Interactive Robotics Unit of CEA LIST has developed a very challenging robotic carrier (called P.A.C.) which is able to perform high range intervention tasks inside blind hot cells. This long reach multi-link carr...
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The Interactive Robotics Unit of CEA LIST has developed a very challenging robotic carrier (called P.A.C.) which is able to perform high range intervention tasks inside blind hot cells. This long reach multi-link carrier has 11 degrees of freedom (DOF) and weighs less than 30 kg. Due to its size and weight, this large robot manipulator holds lots of elastic and geometric deformations. Hence, it presents very low position accuracy. A mechanical model is developed to take into account the flexibilities of the structure. A calibration method of the robot's parameters is used to further reduce the positioning error of the end effector. This method is first tested by simulation. After encouraging simulation results, an experimental field is made for the calibration of the PAC manipulator. Results show that the adopted flexible model, with the new calibrated parameters, is indeed able to correct and reduce for the system errors.
Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computer-implementable form. There are many successful ap...
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Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computer-implementable form. There are many successful applications of this methodology to situations in which expert knowledge exist, the must well known is an application to fuzzy control. In some cases, fuzzy methodology is applied even when no expert knowledge exists: instead of trying to approximate the unknown control function by splines, polynomials, or by another traditional approximation technique, researchers try to approximate it by guessing and tuning the expert rules. Surprisingly, this approximation often works fine. In this paper, we give a mathematical explanation for this phenomenon, and show that approximation by using fuzzy methodology is indeed (in some reasonable sense) the best
An unmanned robotic operating room consists of a tele-operated surgical robot and various other subsystems that perform surgery support functions such as dispensing tools and supplies. Execution Engine for such an ope...
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An unmanned robotic operating room consists of a tele-operated surgical robot and various other subsystems that perform surgery support functions such as dispensing tools and supplies. Execution Engine for such an operating room has to coordinate the subsystems to perform surgery support functions. A support function consists of a series of tasks along with other constructs such as conditional statements. A task, in turn, is composed of subsystem level actions connected by complex synchronization constraints. Previous research has focused on developing a language for specifying tasks and modeling the constraints between them. We present an approach where a task is specified as a data-structure rather than a language. This has the advantage of implicitly encoding the synchronization constraints in the data-structure itself, making it easy to write, debug and maintain task programs. We model a task as a directed acyclic graph and propose an algorithm to execute the task graph. The algorithm is general and language independent, thus eliminating the need for a special purpose language to specify tasks. We discuss implementation results on an emulated system.
Linear Parameter Varying (LPV) systems appear in a form of LTI state space representations where the elements of the A(ρ) , B(ρ) , C(ρ) matrices depend on an unknown but at any time instant measurable vector pa...
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Linear Parameter Varying (LPV) systems appear in a form of LTI state space representations where the elements of the A(ρ) , B(ρ) , C(ρ) matrices depend on an unknown but at any time instant measurable vector parameter ρ ϵ 𝒑. This paper describes a geometric view of LPV systems. Geometric concepts and tools of invariant subspaces and algorithms for LPV systems affine in the parameters will be presented and proposed. Application of these results will be shown and referenced in solving various analysis (controllability/observability) problems, controller design and fault detection problems associated to LPV systems.
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