Formation Control of Car-like Mobile Robots: A Lyapunov Function Based Approach

In literature leader - follower strategy has been used extensively for formation control of car-like mobile robots with the control law being derived from the kinematics. This paper takes it a step further and a nonlinear control law is derived using Lyapunov analysis for formation control of car-like mobile robots using robot dynamics. Controller is split into two parts. The first part is the development of a velocity controller for the follower from the error kinematics (linear and angular). The second part involves the use of the dynamics of the robot in the development of a torque controller for both the drive and the steering system of the car-like mobile robot. Unknown quantities like friction, desired accelerations (unmeasured) are computed using an online neural network. Simulations results prove the ability of the controller to effectively stabilize the formation while maintaining the desired relative distance and bearing

Optimal Control of Class of Non-Linear Plants using Artificial Immune Systems: Application of the Clonal Selection Algorithm

The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear plants which are affine in control. The clonal selection algorithm is adapted for optimal control. A new mutation operator that operates on real values and one that aids in fast convergence is developed in this paper. AIS is used to obtain constant coefficient Kalman gain matrices. The validation and evaluation of the results thus obtained are carried out by comparing with standard and the widely used State Dependent Algebraic Riccati Equation (SDARE) method for the non-linear plants. In case of non-linear systems with hard state constraints, the SDARE formulation requires the use of mathematically involved expressions to incorporate these state constraints. However, the modified clonal selection algorithm developed in this paper has been used with hardly any changes to incorporate the hard state constraints and obtain the Kalman gain matrix