Bach Hoang Dinh

Flocking of mobile robots by bounded feedback

This paper addresses the problem of flocking of mobile robots by bounded feedback. A control problem is formulated with the multiple goals: velocity consensus, collision avoidance, and cohesion maintenance. The flocking protocol is then obtained by means of control design. Particularly, a Lyapunov-like function is presented, the velocity control is designed to achieve coordination and linear speed consensus, and the steering control is designed for consensus on orientation. The convergence is verified via Barbalats lemma. Simulation results with a group of mobile robots are presented to show the effectiveness of the proposed flocking control.

A New Optimal Algorithm for Multi-objective Short-Term Fixed Head Hydrothermal Scheduling with Emission Control Consideration

With the purpose of environment protection, polluted emissions should be considered as an additional criteria, besides the fuel cost objective, to implement the optimal operation of hydrothermal power systems which are included thermal and hydro power plants. This paper proposes a novel cuckoo search algorithm (NCSA), developed from cuckoo-inspired optimization algorithm, to solve the multi-objective short-term fixed-head hydrothermal scheduling (HTS) problem which considers both fuel cost objective and emission cost objectives. In the proposed approach, there are two modifications, merging the exploration and exploitation phases and setting one rank parameter to handle the inequality constraints, to improve the convergence rate and the performance of NCSA. To verify its performance, some test systems with the quadratic fuel cost function of thermal units accompanied with emission objective functions have been implemented. The results have revealed that the NCSA method is a very promising method for solving the multi-objective short-term fixed-head HTS problem.

On-line Training Solution to Modify the Inverse Kinematic Approximation of a Robot Manipulator

This paper describes a new practical approach for approximating the inverse kinematics of a manipulator using an RBFN (Radial Basis Function Network). In fact, sometimes a well-trained network cannot work effectively in the operational phase because the initial network training occurs in an environment that is not exactly the same as the environment where the system is actually deployed. In this paper, an online retraining solution using a “free interference rule” is presented for systems whose characteristics change due to environmental variations. It helps the learning process avoiding the interference where a new training point may upset some of the weights which were trained with previous points. The simulation results prove that the proposed approach is effective.

New Solutions to Modify the Differential Evolution Method for Multi-objective Load Dispatch Problem Considering Quadratic Fuel Cost Function

This paper proposes a Modified Differential Evolution (MDE) method to solve a multi-objective load dispatch (MOLD) problem where generators’ fuel cost and emission are minimized with the power balance and power loss constraints. MDE is an improved version of conventional Differential Evolution (CDE) in which the mutation operation of the CDE is improved by using five differential solutions instead of three ones of CDE. In the MOLD problem, multi-objective dispatch are carried out by considering fuel cost function, emission function and both fuel cost and emission functions where a price penalty factor is employed to determine the best compromise solution. The performance of MDE is verified by doing simulation on two systems, three units and six units, respectively. The results of the MDE method compared to those of CDE and other meta-heuristic algorithms, proved that the MDE is a promising algorithm for solving the MOLD problem.

Slide Mode Control Design for a Class of Uncertain Dynamic Systems

In this paper, the problem of designing a sliding mode control law for uncertain systems where the uncertainty in the state model does not satisfy the matching condition is considered. Based on the linear matrix inequality approach, a sufficient condition is proposed for the existence of linear sliding surfaces guaranteeing that the system in sliding mode is asymptotically stable. Sliding mode control laws are designed such that the system states reach the sliding surface and stay on it thereafter. Finally, the advantages and effectiveness of the proposed approaches are demonstrated via a numerical example.

A cuckoo bird-inspired meta-heuristic algorithm for optimal short-term hydrothermal generation cooperation

Abstract Optimal short-term hydrothermal generation cooperation (OSTHTGC) is used to determine the optimal operation strategy for cascaded hydropower plants and thermal plants. In this problem, equality constraints consisting of water time delay, reservoir volume, continuity water and power balance, as well as inequality constraints related to the limits of thermal and hydro generations are taken into account. In this paper, a Cuckoo Bird-Inspired Meta-Heuristic Algorithm (CBIA), which is advanced by using only few control parameters and high success rate, has been proposed to solve the OSTHTGC problem. The proposed method is applied for four test cases where the valve point loading effects of thermal units and the combination operation of a four-cascaded reservoir system are considered. The comparison of obtained results has revealed that that CBIA method is an efficient method with high quality solution and fast convergence speed compared to other relevant approaches.

An Alternative Method to Approximate the Inverse Kinematics of Unknown Geometry Manipulators Using an RBFN with Regularly-Spaced Position Centres

This paper presents a novel solution using Radial Basis Function Networks (RBFNs) to approximate the inverse kinematics of unknown geometry manipulatos (e.g. a robot-vision system). This approach has two fundamental principles: centres of hidden-layer units are regularly distributed in the workspace and constrained training data is used where inputs are collected around the centre positions in the workspace. To verify the performance of the proposed approach, simulations in Matlab and practical experiment have been performed. The results of both the simulation and experiment prove that the proposed approach is effective.

A radial basis function network approach to approximate the inverse kinematics of a robotic system

This paper presents a novel solution using a radial basis function network (RBFN) to approximate the inverse kinematics of a robotic system where the geometric parameters of the manipulator are unknown. Simulation and experimental results are presented for a three-link manipulator to demonstrate the effectiveness of the proposed approach. To achieve this level of performance, centres of hidden-layer units are regularly distributed in the workspace, constrained training data is used where inputs are collected approximately around the centre positions in the workspace and the training phase is performed using either strict interpolation or the least mean square algorithm. These proposed ideas have significantly improved the networks performance.

Using an RBF network with regularly-spaced position centres to approximate the inverse kinematic of a robot-vision system

This paper presents a novel solution using Radial basis function networks (RBFNs) to approximate the inverse kinematics of a robot-vision system. This approach has two fundamental principles: centres of hidden-layer units are regularly distributed in the workspace and constrained training data is used where inputs are collected around the centre positions in the workspace. To verify the performance of the proposed approach, a practical experiment has been performed using a Mitsubishi PA10-6CE manipulator observed by a webcam. All application programmes, such as robot servo control, neural network, and image processing tool, were written in C/C++ and run in a real robotic system. The experimental results prove that the proposed approach is effective.