This paper deals with the synthesis and implementation of a controller for asymptotic tracking of the desired trajectory of a mobile robot. The mobile robot used for the experimental validation has eight motors with an inner control loop. Four steering actuators are controlled using position controllers and four driving actuators are controlled using velocity controllers. A complex robot kinematic model is converted into a control-oriented linear time-varying system, which is then used to design a time-varying control law that minimizes the quadratic optimality criterion. In contrast to conventional methodologies for solving the corresponding Riccati differential equations, a computational approach that explicitly determines the time-varying controller matrix by employing recurrent matrix computations is proposed. Mobile robot control inputs (linear velocity, steering angles and steering velocities) are forwarded to the steering and driving actuators with properly tuned position and velocity controllers using an inverse kinematic model of the mobile robot. The obtained control law is evaluated on an experimental set-up of a real mobile robot system. The controller is implemented using the Robot Operating System.

In this paper, a high-precision algorithm for parameter identification of nonlinear multivariable dynamic systems is proposed. The proposed computational approach is based on the following assumptions: a) system is nonlinearly parameterized by a vector of unknown system parameters; b) only partial measurement of system state is available; c) there are no state observers; d) initial conditions are unknown except for measurable system states. The identification problem is formulated as a continuous dynamic optimization problem which is discretized by higher-order Adams method and numerically solved by a backward-in-time recurrent algorithm which is similar to the backpropagation-through-time (BPTT) algorithm. The proposed algorithm is especially effective for identification of homogenous spatially distributed nonlinear systems what is demonstrated on the parameter identification of a multi-degree-of-freedom torsional system with nonlinearly parameterized elastic forces, unknown initial velocities and positions measurement only.

This paper is concerned with the control law synthesis for robot manipulators, which guarantees that the effect of the sensor faults is kept under a permissible level, and ensures the stability of the closed-loop system. Based on Lyapunov’s stability analysis, the conditions that enable the application of the simple bisection method in the optimization procedure were derived. The control law, with certain properties that make the construction of the Lyapunov function much easier—and, thus, the determination of stability conditions—was considered. Furthermore, the optimization problem was formulated as a class of problem in which minimization and maximization of the same performance criterion were simultaneously carried out. The algorithm proposed to solve the related zero-sum differential game was based on Newton’s method with recursive matrix relations, in which the first- and second-order derivatives of the objective function are calculated using hyper-dual numbers. The results of this paper were evaluated in simulation on a robot manipulator with three degrees of freedom.