Adaptive Control Of Robotic Systems With Actuator Nonlinearities And Motion Constraints

With the development of the multi-discipline crossed-research realm which involves the information science, mechanical engineering and material science, robotic systems have promising application potential and strong market demand in assisting or even replacing human labors. Therefore, researches on robotic systems have not only a bright application prospect, but also a significant academic value. This dissertation models robots as nonlinear control systems, which contain high degrees of freedom and require strongly coupled motion/force analyses. Researches are launched to tackle uncertainties including actuator nonlinearities and motion constraints in robotic systems and to improve the control performances.The organization of this dissertation is presented as follows. Chapter 1 gives a brief introduction on the research background and its significance. Chapter 2 summarizes basic preliminaries, which consist of the modeling and the control of robots. The main body of this dissertation covers five chapters, that is, from Chapter 3 to Chapter 7. In particular, Chapter 3 and Chapter 4 develop adaptive control methods to investigate robotic systems with different types of unknown actuator nonlinearities; Chapter 5 studies the motion control problem of robotic systems with the generalized actuator nonlinearities. Chapter 6 and Chapter 7 tackle robotic systems with unknown output motion uncertainties and constraints.The above five chapters, respectively, correspond to the following five aspects:1. The problem of dual-arm robots grasping a common object with unknown actuator backlash is investigated. To tackle the nonsmooth backlash nonlinearity, a smooth adaptive backlash inverse is incorporated to compensate the line-segment effect. Moreover, a decentralized robust fuzzy adaptive control is constructed and developed to guarantee the object’s motion and internal forces converge to the predefined values. The stabilities of the signals in the closed-loop system are proved by utilizing the Lyapunov method. In the end, simulations involving dual-arm robot manipulation are conducted to validate the effectiveness of the proposed algorithms.2. A coordinated fuzzy control is developed for multiple robotic arms with actuator hysteresis and motion constraint. To accurately compensate the hysteresis phenomena, the modeling of actuator hysteresis is first integrated into the dynamics of multiple arms system. Then, the adaptive control scheme is introduced to reduce the harmful effects from unknown hysteresis nonlinearities. Subsequently, the issue of the motion constraint is taken into account to avoid the potential collisions in the application. Furthermore, the stability analysis is carried out to guarantee the motion and internal forces converge to the desired values. Simultaneously, the predetermined motion boundary is ensured to be never violated. Finally, comparative results are presented to illustrate the effectiveness of the proposed scheme.3. Robotic systems with unknown generalized actuator nonlinearities are investigated. A novel Nussbaum analysis tool for MIMO systems is established such that unknown time-varying control coefficients are tackled. In contrast to existing literatures, the newly-developed Nussbaum tool focuses on extending the traditional Nussbaum result from one dimensional case to the multiple one. Specifically, not only the multiple unknown input coefficients are extended to the time-varying, but also the limitation of the prior knowledge of coefficients’upper and lower bounds is removed. Furthermore, an adaptive robust controller associated with the proposed tool is presented. The asymptotic tracking control of MIMO robotic systems is guaranteed with the help of the Lyapunov method. Furthermore, a saturated Nussbaum function based approach for robotic systems with unknown actuator dynamics is presented. To eliminate the effect of the control shock from the traditional Nussbaum function, a new type of the saturated Nussbaum function is developed with the idea of Time-Elongation. Moreover, by exploiting properties of the proposed Nussbaum function, a promising theorem is established to deal with unknown multiple actuator nonlinearities. In what follows, the proposed theorem is integrated with the adaptive control technique such that the stability analysis of the robotic system is completed. It thus guarantees that the state of the robotic system asymptotically converges to the desired trajectory.4. To address the control problem of output motion constraint from the dual-arm manipulation, a coordinated motion/force adaptive neural network control is proposed.The characteristic of output hysteresis constraint is first analyzed. Then, Nussbaum function based approach is used to handle the nonlinear control gain from the hysteresis constraint during the backstepping based control design. Moreover, the norm of the weight matrix in the neural network is estimated and thus the number of the adaptive laws drops in the proposed approach. Finally, comparisons between the proposed control and the traditional control are carried out. The comparative results further show the effectiveness, superiority and robustness of the proposed scheme.5. A multiple-arm control scheme is proposed to handle the control problem of unknown output motion dead nonlinearities and the manipulated object’s uncertainties. Different from existing literatures on modeling deadzone, the proposed control develops a novel deadzone model and removes the control difficulties from the traditional deadzone models in the backstepping control design. Moreover, the kinematics and dynamics of the manipulated object are assumed unknown in the control design. With the help of Lyapunov’s stability theory, the proposed method ensures the motion and internal force errors bounded in a small neighbourhood around the origin. Finally, comparative studies are presented to show the effectiveness and robustness of the proposed scheme.