Wheeled mobile robots (WMRs) with variable wheelbases are capable of traveling on deformable terrains and handling complex detection tasks. While the variable wheelbase length of WMR allows it to interact with the terrains adaptively, enhancing its mobility, it brings a control challenge. Inspired by the worm's movement of stretching body at different lengths under different environmental resistance, a creeping gait (CG) strategy is proposed in this work to enable the WMR to be controlled in dual modes: wheeled following mode (WFM) and specified length mode (SLM). WFM adjusts the wheelbase's length by the wheels' movements freely to minimize the internal force and torque between wheels. SLM adjusts the wheelbase's length using a proposed fuzzy logic based algorithm to stabilize the body's posture on rough terrain and overcome specific motion challenges, like escaping wheel sinking. A state-adaptive mode-switching controller is then developed using the dwell time approach to smooth the output velocities during the switching phase, and a Lyapunov analysis is performed to verify its stability. According to the results of physical experiments, three-wheeled mobile robot movements with CG enable more precise path following by 37% and faster response by 11% compared to fixed wheelbase movements, and the dwell time approach achieves smoother speed transitions between the modes than the direct switching method, especially when moving from flat to slope terrain.

This article proposes an adaptive finite-time tracking control based on fuzzy-logic systems (FLSs) for an uncertain nonstrict nonlinear multi-input-multi-output (MIMO) full-state-constrained system with the coupled uncertain dead-zone input. By using three kinds of FLSs: the uncertain system, the uncertain dead zone, and the uncertain input transfer inverse matrix are approximated using the system function FLS, dead-zone FLS, and input transfer inverse matrix FLS, respectively. After defining the barrier Lyapunov function, the fuzzy-based adaptive tracking controllers are designed, and the fuzzy weights are updated through the proposed adaptive laws. Then, based on the extended finite-time convergence theorem, with the design parameters chosen properly, the target uncertain nonlinear system is guaranteed to be semiglobal practical finite-time stable (SGPFS); and the full-state constraints are not violated while avoiding the effects of the dead zones. Furthermore, a simulation is presented to verify the validity of the proposed algorithm.

Considering the uncertain nonstrict nonlinear system with dead-zone input, an adaptive neural network (NN)-based finite-time online optimal tracking control algorithm is proposed. By using the tracking errors and the Lipschitz linearized desired tracking function as the new state vector, an extended system is present. Then, a novel Hamilton-Jacobi-Bellman (HJB) function is defined to associate with the nonquadratic performance function. Further, the upper limit of integration is selected as the finite-time convergence time, in which the dead-zone input is considered. In addition, the Bellman error function can be obtained from the Hamiltonian function. Then, the adaptations of the critic and action NN are updated by using the gradient descent method on the Bellman error function. The semiglobal practical finite-time stability (SGPFS) is guaranteed, and the tracking errors convergence to a compact set by zero in a finite time.

In this paper, an adaptive dynamic programming-based online adaptive tracking control algorithm is proposed to solve the tracking problem of the partial uncertain time-delayed nonlinear affine system with uncertain resistance. Using the discrete-time Hamilton-Jacobi-Bellman function, the input time-delay separation lemma, and the Lyapunov-Krasovskii functionals, the partial state and input time delay can be determined. With the approximation of the action and critic, and resistance neural networks, a near-optimal controller and appropriate adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the target system, and the tracking error convergence to a small compact set to zero. A numerical simulation of the wheeled mobile robotic system is presented to verify the validity of the proposed method.

Resistance coefficient (RC) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as a constant that depends on the wheel load, wheel dimensions, and soil that the WMR is designed for. This article proposes a novel variable RC that responds to terrain deformation. This variable RC is then applied to controllers for WMRs that estimate driving torques and slip ratios on deformable terrain. Simple yet accurate models of RC are developed from both experimental results and theoretical analysis, and these models are then compared with other methods. The proposed RC models give more accurate and more computationally efficient estimations of driving torques and slip ratios for WMRs, with average estimation errors less than 6% and the shortest computation time in experiments. The two proposed estimators are then applied to the design of the tracking-control systems for a WMR running on deformable terrain. Experiments with simulated sandy terrain demonstrate that both proposed control systems are feasible, and the slip estimation effectively decreases velocity tracking errors from more than 20% to less than 10%.

Simulation of planetary rovers moving on complex terrains is critical for Mars exploration. Equivalent stiffness is proposed and used to characterize the pressure-sinkage property of terrain, while friction angle to characterize the shearing property. Terramechanics model for calculating forces between rigid wheel and soil is proved to be the same with that contact model for calculating forces between rigid wheel and rock. A Digital Elevation Map with Physical Properties is developed and applied to simulate terrain physical properties along with its geometry information. The established methods are validated using simulation and experimental tests with a three-wheel-rover.